Medical error identification, disclosure, and reporting: do emergency medicine provider groups differ?

PubMed

Hobgood, Cherri; Weiner, Bryan; Tamayo-Sarver, Joshua H

2006-04-01

To determine if the three types of emergency medicine providers--physicians, nurses, and out-of-hospital providers (emergency medical technicians [EMTs])--differ in their identification, disclosure, and reporting of medical error. A convenience sample of providers in an academic emergency department evaluated ten case vignettes that represented two error types (medication and cognitive) and three severity levels. For each vignette, providers were asked the following: 1) Is this an error? 2) Would you tell the patient? 3) Would you report this to a hospital committee? To assess differences in identification, disclosure, and reporting by provider type, error type, and error severity, the authors constructed three-way tables with the nonparametric Somers' D clustered on participant. To assess the contribution of disclosure instruction and environmental variables, fixed-effects regression stratified by provider type was used. Of the 116 providers who were eligible, 103 (40 physicians, 26 nurses, and 35 EMTs) had complete data. Physicians were more likely to classify an event as an error (78%) than nurses (71%; p = 0.04) or EMTs (68%; p < 0.01). Nurses were less likely to disclose an error to the patient (59%) than physicians (71%; p = 0.04). Physicians were the least likely to report the error (54%) compared with nurses (68%; p = 0.02) or EMTs (78%; p < 0.01). For all provider and error types, identification, disclosure, and reporting increased with increasing severity. Improving patient safety hinges on the ability of health care providers to accurately identify, disclose, and report medical errors. Interventions must account for differences in error identification, disclosure, and reporting by provider type.

Estimating cell populations

NASA Technical Reports Server (NTRS)

White, B. S.; Castleman, K. R.

1981-01-01

An important step in the diagnosis of a cervical cytology specimen is estimating the proportions of the various cell types present. This is usually done with a cell classifier, the error rates of which can be expressed as a confusion matrix. We show how to use the confusion matrix to obtain an unbiased estimate of the desired proportions. We show that the mean square error of this estimate depends on a 'befuddlement matrix' derived from the confusion matrix, and how this, in turn, leads to a figure of merit for cell classifiers. Finally, we work out the two-class problem in detail and present examples to illustrate the theory.

Corrective Techniques and Future Directions for Treatment of Residual Refractive Error Following Cataract Surgery

PubMed Central

Moshirfar, Majid; McCaughey, Michael V; Santiago-Caban, Luis

2015-01-01

Postoperative residual refractive error following cataract surgery is not an uncommon occurrence for a large proportion of modern-day patients. Residual refractive errors can be broadly classified into 3 main categories: myopic, hyperopic, and astigmatic. The degree to which a residual refractive error adversely affects a patient is dependent on the magnitude of the error, as well as the specific type of intraocular lens the patient possesses. There are a variety of strategies for resolving residual refractive errors that must be individualized for each specific patient scenario. In this review, the authors discuss contemporary methods for rectification of residual refractive error, along with their respective indications/contraindications, and efficacies. PMID:25663845

Corrective Techniques and Future Directions for Treatment of Residual Refractive Error Following Cataract Surgery.

PubMed

Moshirfar, Majid; McCaughey, Michael V; Santiago-Caban, Luis

2014-12-01

Postoperative residual refractive error following cataract surgery is not an uncommon occurrence for a large proportion of modern-day patients. Residual refractive errors can be broadly classified into 3 main categories: myopic, hyperopic, and astigmatic. The degree to which a residual refractive error adversely affects a patient is dependent on the magnitude of the error, as well as the specific type of intraocular lens the patient possesses. There are a variety of strategies for resolving residual refractive errors that must be individualized for each specific patient scenario. In this review, the authors discuss contemporary methods for rectification of residual refractive error, along with their respective indications/contraindications, and efficacies.

Types of diagnostic errors in neurological emergencies in the emergency department.

PubMed

Dubosh, Nicole M; Edlow, Jonathan A; Lefton, Micah; Pope, Jennifer V

2015-02-01

Neurological emergencies often pose diagnostic challenges for emergency physicians because these patients often present with atypical symptoms and standard imaging tests are imperfect. Misdiagnosis occurs due to a variety of errors. These can be classified as knowledge gaps, cognitive errors, and systems-based errors. The goal of this study was to describe these errors through review of quality assurance (QA) records. This was a retrospective pilot study of patients with neurological emergency diagnoses that were missed or delayed at one urban, tertiary academic emergency department. Cases meeting inclusion criteria were identified through review of QA records. Three emergency physicians independently reviewed each case and determined the type of error that led to the misdiagnosis. Proportions, confidence intervals, and a reliability coefficient were calculated. During the study period, 1168 cases were reviewed. Forty-two cases were found to include a neurological misdiagnosis and twenty-nine were determined to be the result of an error. The distribution of error types was as follows: knowledge gap 45.2% (95% CI 29.2, 62.2), cognitive error 29.0% (95% CI 15.9, 46.8), and systems-based error 25.8% (95% CI 13.5, 43.5). Cerebellar strokes were the most common type of stroke misdiagnosed, accounting for 27.3% of missed strokes. All three error types contributed to the misdiagnosis of neurological emergencies. Misdiagnosis of cerebellar lesions and erroneous radiology resident interpretations of neuroimaging were the most common mistakes. Understanding the types of errors may enable emergency physicians to develop possible solutions and avoid them in the future.

The Potential of AutoClass as an Asteroidal Data Mining Tool

NASA Astrophysics Data System (ADS)

Walker, Matthew; Ziffer, J.; Harvell, T.; Fernandez, Y. R.; Campins, H.

2011-05-01

AutoClass-C, an artificial intelligence program designed to classify large data sets, was developed by NASA to classify stars based upon their infrared colors. Wanting to investigate its ability to classify asteroidal data, we conducted a preliminary test to determine if it could accurately reproduce the Tholen taxonomy using the data from the Eight Color Asteroid Survey (ECAS). For our initial test, we limited ourselves to those asteroids belonging to S, C, or X classes, and to asteroids with a color difference error of less than +/- 0.05 magnitudes. Of those 406 asteroids, AutoClass was able to confidently classify 85%: identifying the remaining asteroids as belonging to more than one class. Of the 346 asteroids that AutoClass classified, all but 3 (<1%) were classified as they had been in the Tholen classification scheme. Inspired by our initial success, we reran AutoClass, this time including IRAS albedos and limiting the asteroids to those that had also been observed and classified in the Bus taxonomy. Of those 258 objects, AutoClass was able to classify 248 with greater than 75% certainty, and ranked albedo, not color, as the most influential factor. Interestingly, AutoClass consistently put P type objects in with the C class (there were 19 P types and 7 X types mixed in with the other 154 C types), and omitted P types from the group associated with the other X types (which had only one rogue B type in with its other 49 X-types). Autoclass classified the remaining classes with a high accuracy: placing one A and one CU type in with an otherwise perfect S group; placing three P type and one T type in an otherwise perfect D group; and placing the four remaining asteroids (V, A, R, and Q) into a class together.

Goal-oriented explicit residual-type error estimates in XFEM

NASA Astrophysics Data System (ADS)

Rüter, Marcus; Gerasimov, Tymofiy; Stein, Erwin

2013-08-01

A goal-oriented a posteriori error estimator is derived to control the error obtained while approximately evaluating a quantity of engineering interest, represented in terms of a given linear or nonlinear functional, using extended finite elements of Q1 type. The same approximation method is used to solve the dual problem as required for the a posteriori error analysis. It is shown that for both problems to be solved numerically the same singular enrichment functions can be used. The goal-oriented error estimator presented can be classified as explicit residual type, i.e. the residuals of the approximations are used directly to compute upper bounds on the error of the quantity of interest. This approach therefore extends the explicit residual-type error estimator for classical energy norm error control as recently presented in Gerasimov et al. (Int J Numer Meth Eng 90:1118-1155, 2012a). Without loss of generality, the a posteriori error estimator is applied to the model problem of linear elastic fracture mechanics. Thus, emphasis is placed on the fracture criterion, here the J-integral, as the chosen quantity of interest. Finally, various illustrative numerical examples are presented where, on the one hand, the error estimator is compared to its finite element counterpart and, on the other hand, improved enrichment functions, as introduced in Gerasimov et al. (2012b), are discussed.

Classifying nursing errors in clinical management within an Australian hospital.

PubMed

Tran, D T; Johnson, M

2010-12-01

Although many classification systems relating to patient safety exist, no taxonomy was identified that classified nursing errors in clinical management. To develop a classification system for nursing errors relating to clinical management (NECM taxonomy) and to describe contributing factors and patient consequences. We analysed 241 (11%) self-reported incidents relating to clinical management in nursing in a metropolitan hospital. Descriptive analysis of numeric data and content analysis of text data were undertaken to derive the NECM taxonomy, contributing factors and consequences for patients. Clinical management incidents represented 1.63 incidents per 1000 occupied bed days. The four themes of the NECM taxonomy were nursing care process (67%), communication (22%), administrative process (5%), and knowledge and skill (6%). Half of the incidents did not cause any patient harm. Contributing factors (n=111) included the following: patient clinical, social conditions and behaviours (27%); resources (22%); environment and workload (18%); other health professionals (15%); communication (13%); and nurse's knowledge and experience (5%). The NECM taxonomy provides direction to clinicians and managers on areas in clinical management that are most vulnerable to error, and therefore, priorities for system change management. Any nurses who wish to classify nursing errors relating to clinical management could use these types of errors. This study informs further research into risk management behaviour, and self-assessment tools for clinicians. Globally, nurses need to continue to monitor and act upon patient safety issues. © 2010 The Authors. International Nursing Review © 2010 International Council of Nurses.

Statistical text classifier to detect specific type of medical incidents.

PubMed

Wong, Zoie Shui-Yee; Akiyama, Masanori

2013-01-01

WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data

NASA Astrophysics Data System (ADS)

Zhu, Likai; Radeloff, Volker C.; Ives, Anthony R.

2017-06-01

Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat's sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p < 0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research.

Working memory load impairs the evaluation of behavioral errors in the medial frontal cortex.

PubMed

Maier, Martin E; Steinhauser, Marco

2017-10-01

Early error monitoring in the medial frontal cortex enables error detection and the evaluation of error significance, which helps prioritize adaptive control. This ability has been assumed to be independent from central capacity, a limited pool of resources assumed to be involved in cognitive control. The present study investigated whether error evaluation depends on central capacity by measuring the error-related negativity (Ne/ERN) in a flanker paradigm while working memory load was varied on two levels. We used a four-choice flanker paradigm in which participants had to classify targets while ignoring flankers. Errors could be due to responding either to the flankers (flanker errors) or to none of the stimulus elements (nonflanker errors). With low load, the Ne/ERN was larger for flanker errors than for nonflanker errors-an effect that has previously been interpreted as reflecting differential significance of these error types. With high load, no such effect of error type on the Ne/ERN was observable. Our findings suggest that working memory load does not impair the generation of an Ne/ERN per se but rather impairs the evaluation of error significance. They demonstrate that error monitoring is composed of capacity-dependent and capacity-independent mechanisms. © 2017 Society for Psychophysiological Research.

Syntactic and semantic errors in radiology reports associated with speech recognition software.

PubMed

Ringler, Michael D; Goss, Brian C; Bartholmai, Brian J

2017-03-01

Speech recognition software can increase the frequency of errors in radiology reports, which may affect patient care. We retrieved 213,977 speech recognition software-generated reports from 147 different radiologists and proofread them for errors. Errors were classified as "material" if they were believed to alter interpretation of the report. "Immaterial" errors were subclassified as intrusion/omission or spelling errors. The proportion of errors and error type were compared among individual radiologists, imaging subspecialty, and time periods. In all, 20,759 reports (9.7%) contained errors, of which 3992 (1.9%) were material errors. Among immaterial errors, spelling errors were more common than intrusion/omission errors ( p < .001). Proportion of errors and fraction of material errors varied significantly among radiologists and between imaging subspecialties ( p < .001). Errors were more common in cross-sectional reports, reports reinterpreting results of outside examinations, and procedural studies (all p < .001). Error rate decreased over time ( p < .001), which suggests that a quality control program with regular feedback may reduce errors.

Analysis and application of classification methods of complex carbonate reservoirs

NASA Astrophysics Data System (ADS)

Li, Xiongyan; Qin, Ruibao; Ping, Haitao; Wei, Dan; Liu, Xiaomei

2018-06-01

There are abundant carbonate reservoirs from the Cenozoic to Mesozoic era in the Middle East. Due to variation in sedimentary environment and diagenetic process of carbonate reservoirs, several porosity types coexist in carbonate reservoirs. As a result, because of the complex lithologies and pore types as well as the impact of microfractures, the pore structure is very complicated. Therefore, it is difficult to accurately calculate the reservoir parameters. In order to accurately evaluate carbonate reservoirs, based on the pore structure evaluation of carbonate reservoirs, the classification methods of carbonate reservoirs are analyzed based on capillary pressure curves and flow units. Based on the capillary pressure curves, although the carbonate reservoirs can be classified, the relationship between porosity and permeability after classification is not ideal. On the basis of the flow units, the high-precision functional relationship between porosity and permeability after classification can be established. Therefore, the carbonate reservoirs can be quantitatively evaluated based on the classification of flow units. In the dolomite reservoirs, the average absolute error of calculated permeability decreases from 15.13 to 7.44 mD. Similarly, the average absolute error of calculated permeability of limestone reservoirs is reduced from 20.33 to 7.37 mD. Only by accurately characterizing pore structures and classifying reservoir types, reservoir parameters could be calculated accurately. Therefore, characterizing pore structures and classifying reservoir types are very important to accurate evaluation of complex carbonate reservoirs in the Middle East.

Scheduling periodic jobs that allow imprecise results

NASA Technical Reports Server (NTRS)

Chung, Jen-Yao; Liu, Jane W. S.; Lin, Kwei-Jay

1990-01-01

The problem of scheduling periodic jobs in hard real-time systems that support imprecise computations is discussed. Two workload models of imprecise computations are presented. These models differ from traditional models in that a task may be terminated any time after it has produced an acceptable result. Each task is logically decomposed into a mandatory part followed by an optional part. In a feasible schedule, the mandatory part of every task is completed before the deadline of the task. The optional part refines the result produced by the mandatory part to reduce the error in the result. Applications are classified as type N and type C, according to undesirable effects of errors. The two workload models characterize the two types of applications. The optional parts of the tasks in an N job need not ever be completed. The resulting quality of each type-N job is measured in terms of the average error in the results over several consecutive periods. A class of preemptive, priority-driven algorithms that leads to feasible schedules with small average error is described and evaluated.

Identification and correction of systematic error in high-throughput sequence data

PubMed Central

2011-01-01

Background A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. Results We characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets. Conclusions Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments. PMID:22099972

Fisher classifier and its probability of error estimation

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

Computationally efficient expressions are derived for estimating the probability of error using the leave-one-out method. The optimal threshold for the classification of patterns projected onto Fisher's direction is derived. A simple generalization of the Fisher classifier to multiple classes is presented. Computational expressions are developed for estimating the probability of error of the multiclass Fisher classifier.

Microscale photo interpretation of forest and nonforest land classes

NASA Technical Reports Server (NTRS)

Aldrich, R. C.; Greentree, W. J.

1972-01-01

Remote sensing of forest and nonforest land classes are discussed, using microscale photointerpretation. Results include: (1.) Microscale IR color photography can be interpreted within reasonable limits of error to estimate forest area. (2.) Forest interpretation is best on winter photography with 97 percent or better accuracy. (3.) Broad forest types can be classified on microscale photography. (4.) Active agricultural land is classified most accurately on early summer photography. (5.) Six percent of all nonforest observations were misclassified as forest.

Evaluation of drug administration errors in a teaching hospital

PubMed Central

2012-01-01

Background Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. We aimed to determine the incidence, type and clinical importance of drug administration errors and to identify risk factors. Methods Prospective study based on disguised observation technique in four wards in a teaching hospital in Paris, France (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. Main outcomes were number, type and clinical importance of errors and associated risk factors. Drug administration error rate was calculated with and without wrong time errors. Relationship between the occurrence of errors and potential risk factors were investigated using logistic regression models with random effects. Results Twenty-eight nurses caring for 108 patients were observed. Among 1501 opportunities for error, 415 administrations (430 errors) with one or more errors were detected (27.6%). There were 312 wrong time errors, ten simultaneously with another type of error, resulting in an error rate without wrong time error of 7.5% (113/1501). The most frequently administered drugs were the cardiovascular drugs (425/1501, 28.3%). The highest risks of error in a drug administration were for dermatological drugs. No potentially life-threatening errors were witnessed and 6% of errors were classified as having a serious or significant impact on patients (mainly omission). In multivariate analysis, the occurrence of errors was associated with drug administration route, drug classification (ATC) and the number of patient under the nurse's care. Conclusion Medication administration errors are frequent. The identification of its determinants helps to undertake designed interventions. PMID:22409837

Evaluation of drug administration errors in a teaching hospital.

PubMed

Berdot, Sarah; Sabatier, Brigitte; Gillaizeau, Florence; Caruba, Thibaut; Prognon, Patrice; Durieux, Pierre

2012-03-12

Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. We aimed to determine the incidence, type and clinical importance of drug administration errors and to identify risk factors. Prospective study based on disguised observation technique in four wards in a teaching hospital in Paris, France (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. Main outcomes were number, type and clinical importance of errors and associated risk factors. Drug administration error rate was calculated with and without wrong time errors. Relationship between the occurrence of errors and potential risk factors were investigated using logistic regression models with random effects. Twenty-eight nurses caring for 108 patients were observed. Among 1501 opportunities for error, 415 administrations (430 errors) with one or more errors were detected (27.6%). There were 312 wrong time errors, ten simultaneously with another type of error, resulting in an error rate without wrong time error of 7.5% (113/1501). The most frequently administered drugs were the cardiovascular drugs (425/1501, 28.3%). The highest risks of error in a drug administration were for dermatological drugs. No potentially life-threatening errors were witnessed and 6% of errors were classified as having a serious or significant impact on patients (mainly omission). In multivariate analysis, the occurrence of errors was associated with drug administration route, drug classification (ATC) and the number of patient under the nurse's care. Medication administration errors are frequent. The identification of its determinants helps to undertake designed interventions.

Scheduling periodic jobs using imprecise results

NASA Technical Reports Server (NTRS)

Chung, Jen-Yao; Liu, Jane W. S.; Lin, Kwei-Jay

1987-01-01

One approach to avoid timing faults in hard, real-time systems is to make available intermediate, imprecise results produced by real-time processes. When a result of the desired quality cannot be produced in time, an imprecise result of acceptable quality produced before the deadline can be used. The problem of scheduling periodic jobs to meet deadlines on a system that provides the necessary programming language primitives and run-time support for processes to return imprecise results is discussed. Since the scheduler may choose to terminate a task before it is completed, causing it to produce an acceptable but imprecise result, the amount of processor time assigned to any task in a valid schedule can be less than the amount of time required to complete the task. A meaningful formulation of the scheduling problem must take into account the overall quality of the results. Depending on the different types of undesirable effects caused by errors, jobs are classified as type N or type C. For type N jobs, the effects of errors in results produced in different periods are not cumulative. A reasonable performance measure is the average error over all jobs. Three heuristic algorithms that lead to feasible schedules with small average errors are described. For type C jobs, the undesirable effects of errors produced in different periods are cumulative. Schedulability criteria of type C jobs are discussed.

Class-specific Error Bounds for Ensemble Classifiers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prenger, R; Lemmond, T; Varshney, K

2009-10-06

The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missedmore » detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.« less

Frequency and analysis of non-clinical errors made in radiology reports using the National Integrated Medical Imaging System voice recognition dictation software.

PubMed

Motyer, R E; Liddy, S; Torreggiani, W C; Buckley, O

2016-11-01

Voice recognition (VR) dictation of radiology reports has become the mainstay of reporting in many institutions worldwide. Despite benefit, such software is not without limitations, and transcription errors have been widely reported. Evaluate the frequency and nature of non-clinical transcription error using VR dictation software. Retrospective audit of 378 finalised radiology reports. Errors were counted and categorised by significance, error type and sub-type. Data regarding imaging modality, report length and dictation time was collected. 67 (17.72 %) reports contained ≥1 errors, with 7 (1.85 %) containing 'significant' and 9 (2.38 %) containing 'very significant' errors. A total of 90 errors were identified from the 378 reports analysed, with 74 (82.22 %) classified as 'insignificant', 7 (7.78 %) as 'significant', 9 (10 %) as 'very significant'. 68 (75.56 %) errors were 'spelling and grammar', 20 (22.22 %) 'missense' and 2 (2.22 %) 'nonsense'. 'Punctuation' error was most common sub-type, accounting for 27 errors (30 %). Complex imaging modalities had higher error rates per report and sentence. Computed tomography contained 0.040 errors per sentence compared to plain film with 0.030. Longer reports had a higher error rate, with reports >25 sentences containing an average of 1.23 errors per report compared to 0-5 sentences containing 0.09. These findings highlight the limitations of VR dictation software. While most error was deemed insignificant, there were occurrences of error with potential to alter report interpretation and patient management. Longer reports and reports on more complex imaging had higher error rates and this should be taken into account by the reporting radiologist.

Prevalence of medication errors in primary health care at Bahrain Defence Force Hospital – prescription-based study

PubMed Central

Aljasmi, Fatema; Almalood, Fatema

2018-01-01

Background One of the important activities that physicians – particularly general practitioners – perform is prescribing. It occurs in most health care facilities and especially in primary health care (PHC) settings. Objectives This study aims to determine what types of prescribing errors are made in PHC at Bahrain Defence Force (BDF) Hospital, and how common they are. Methods This was a retrospective study of data from PHC at BDF Hospital. The data consisted of 379 prescriptions randomly selected from the pharmacy between March and May 2013, and errors in the prescriptions were classified into five types: major omission, minor omission, commission, integration, and skill-related errors. Results Of the total prescriptions, 54.4% (N=206) were given to male patients and 45.6% (N=173) to female patients; 24.8% were given to patients under the age of 10 years. On average, there were 2.6 drugs per prescription. In the prescriptions, 8.7% of drugs were prescribed by their generic names, and 28% (N=106) of prescriptions included an antibiotic. Out of the 379 prescriptions, 228 had an error, and 44.3% (N=439) of the 992 prescribed drugs contained errors. The proportions of errors were as follows: 9.9% (N=38) were minor omission errors; 73.6% (N=323) were major omission errors; 9.3% (N=41) were commission errors; and 17.1% (N=75) were skill-related errors. Conclusion This study provides awareness of the presence of prescription errors and frequency of the different types of errors that exist in this hospital. Understanding the different types of errors could help future studies explore the causes of specific errors and develop interventions to reduce them. Further research should be conducted to understand the causes of these errors and demonstrate whether the introduction of electronic prescriptions has an effect on patient outcomes. PMID:29445304

Concomitant prescribing and dispensing errors at a Brazilian hospital: a descriptive study

PubMed Central

Silva, Maria das Dores Graciano; Rosa, Mário Borges; Franklin, Bryony Dean; Reis, Adriano Max Moreira; Anchieta, Lêni Márcia; Mota, Joaquim Antônio César

2011-01-01

OBJECTIVE: To analyze the prevalence and types of prescribing and dispensing errors occurring with high-alert medications and to propose preventive measures to avoid errors with these medications. INTRODUCTION: The prevalence of adverse events in health care has increased, and medication errors are probably the most common cause of these events. Pediatric patients are known to be a high-risk group and are an important target in medication error prevention. METHODS: Observers collected data on prescribing and dispensing errors occurring with high-alert medications for pediatric inpatients in a university hospital. In addition to classifying the types of error that occurred, we identified cases of concomitant prescribing and dispensing errors. RESULTS: One or more prescribing errors, totaling 1,632 errors, were found in 632 (89.6%) of the 705 high-alert medications that were prescribed and dispensed. We also identified at least one dispensing error in each high-alert medication dispensed, totaling 1,707 errors. Among these dispensing errors, 723 (42.4%) content errors occurred concomitantly with the prescribing errors. A subset of dispensing errors may have occurred because of poor prescription quality. The observed concomitancy should be examined carefully because improvements in the prescribing process could potentially prevent these problems. CONCLUSION: The system of drug prescribing and dispensing at the hospital investigated in this study should be improved by incorporating the best practices of medication safety and preventing medication errors. High-alert medications may be used as triggers for improving the safety of the drug-utilization system. PMID:22012039

Using the Abstraction Network in Complement to Description Logics for Quality Assurance in Biomedical Terminologies - A Case Study in SNOMED CT

PubMed Central

Wei, Duo; Bodenreider, Olivier

2015-01-01

Objectives To investigate errors identified in SNOMED CT by human reviewers with help from the Abstraction Network methodology and examine why they had escaped detection by the Description Logic (DL) classifier. Case study; Two examples of errors are presented in detail (one missing IS-A relation and one duplicate concept). After correction, SNOMED CT is reclassified to ensure that no new inconsistency was introduced. Conclusions DL-based auditing techniques built in terminology development environments ensure the logical consistency of the terminology. However, complementary approaches are needed for identifying and addressing other types of errors. PMID:20841848

Using the abstraction network in complement to description logics for quality assurance in biomedical terminologies - a case study in SNOMED CT.

PubMed

Wei, Duo; Bodenreider, Olivier

2010-01-01

To investigate errors identified in SNOMED CT by human reviewers with help from the Abstraction Network methodology and examine why they had escaped detection by the Description Logic (DL) classifier. Case study; Two examples of errors are presented in detail (one missing IS-A relation and one duplicate concept). After correction, SNOMED CT is reclassified to ensure that no new inconsistency was introduced. DL-based auditing techniques built in terminology development environments ensure the logical consistency of the terminology. However, complementary approaches are needed for identifying and addressing other types of errors.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Confidential reporting of patient safety events in primary care: results from a multilevel classification of cognitive and system factors.

PubMed

Kostopoulou, Olga; Delaney, Brendan

2007-04-01

To classify events of actual or potential harm to primary care patients using a multilevel taxonomy of cognitive and system factors. Observational study of patient safety events obtained via a confidential but not anonymous reporting system. Reports were followed up with interviews where necessary. Events were analysed for their causes and contributing factors using causal trees and were classified using the taxonomy. Five general medical practices in the West Midlands were selected to represent a range of sizes and types of patient population. All practice staff were invited to report patient safety events. Main outcome measures were frequencies of clinical types of events reported, cognitive types of error, types of detection and contributing factors; and relationship between types of error, practice size, patient consequences and detection. 78 reports were relevant to patient safety and analysable. They included 21 (27%) adverse events and 50 (64%) near misses. 16.7% (13/71) had serious patient consequences, including one death. 75.7% (59/78) had the potential for serious patient harm. Most reports referred to administrative errors (25.6%, 20/78). 60% (47/78) of the reports contained sufficient information to characterise cognition: "situation assessment and response selection" was involved in 45% (21/47) of these reports and was often linked to serious potential consequences. The most frequent contributing factor was work organisation, identified in 71 events. This included excessive task demands (47%, 37/71) and fragmentation (28%, 22/71). Even though most reported events were near misses, events with serious patient consequences were also reported. Failures in situation assessment and response selection, a cognitive activity that occurs in both clinical and administrative tasks, was related to serious potential harm.

Confidential reporting of patient safety events in primary care: results from a multilevel classification of cognitive and system factors

PubMed Central

Kostopoulou, Olga; Delaney, Brendan

2007-01-01

Objective To classify events of actual or potential harm to primary care patients using a multilevel taxonomy of cognitive and system factors. Methods Observational study of patient safety events obtained via a confidential but not anonymous reporting system. Reports were followed up with interviews where necessary. Events were analysed for their causes and contributing factors using causal trees and were classified using the taxonomy. Five general medical practices in the West Midlands were selected to represent a range of sizes and types of patient population. All practice staff were invited to report patient safety events. Main outcome measures were frequencies of clinical types of events reported, cognitive types of error, types of detection and contributing factors; and relationship between types of error, practice size, patient consequences and detection. Results 78 reports were relevant to patient safety and analysable. They included 21 (27%) adverse events and 50 (64%) near misses. 16.7% (13/71) had serious patient consequences, including one death. 75.7% (59/78) had the potential for serious patient harm. Most reports referred to administrative errors (25.6%, 20/78). 60% (47/78) of the reports contained sufficient information to characterise cognition: “situation assessment and response selection” was involved in 45% (21/47) of these reports and was often linked to serious potential consequences. The most frequent contributing factor was work organisation, identified in 71 events. This included excessive task demands (47%, 37/71) and fragmentation (28%, 22/71). Conclusions Even though most reported events were near misses, events with serious patient consequences were also reported. Failures in situation assessment and response selection, a cognitive activity that occurs in both clinical and administrative tasks, was related to serious potential harm. PMID:17403753

An investigation of the usability of sound recognition for source separation of packaging wastes in reverse vending machines.

PubMed

Korucu, M Kemal; Kaplan, Özgür; Büyük, Osman; Güllü, M Kemal

2016-10-01

In this study, we investigate the usability of sound recognition for source separation of packaging wastes in reverse vending machines (RVMs). For this purpose, an experimental setup equipped with a sound recording mechanism was prepared. Packaging waste sounds generated by three physical impacts such as free falling, pneumatic hitting and hydraulic crushing were separately recorded using two different microphones. To classify the waste types and sizes based on sound features of the wastes, a support vector machine (SVM) and a hidden Markov model (HMM) based sound classification systems were developed. In the basic experimental setup in which only free falling impact type was considered, SVM and HMM systems provided 100% classification accuracy for both microphones. In the expanded experimental setup which includes all three impact types, material type classification accuracies were 96.5% for dynamic microphone and 97.7% for condenser microphone. When both the material type and the size of the wastes were classified, the accuracy was 88.6% for the microphones. The modeling studies indicated that hydraulic crushing impact type recordings were very noisy for an effective sound recognition application. In the detailed analysis of the recognition errors, it was observed that most of the errors occurred in the hitting impact type. According to the experimental results, it can be said that the proposed novel approach for the separation of packaging wastes could provide a high classification performance for RVMs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.

PubMed

Pavlovich, Matthew J; Dunn, Emily E; Hall, Adam B

2016-05-15

Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand. Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples. Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models. Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives. Copyright © 2016 John Wiley & Sons, Ltd.

An Analysis of Fifth-Grade Students' Performance When Solving Selected Open Distributive Sentences. Technical Report No. 397.

ERIC Educational Resources Information Center

Hobbs, Charles Eugene

The author investigates elementary school students' performance when solving selected open distributive sentences in relation to three factors (Open Sentence Type, Context, Number Size) and identifies and classifies solution methods attempted by students and students' errors in performance. Eighty fifth-grade students participated in the…

A Generic Deep-Learning-Based Approach for Automated Surface Inspection.

PubMed

Ren, Ruoxu; Hung, Terence; Tan, Kay Chen

2018-03-01

Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Medication Errors in Pediatric Anesthesia: A Report From the Wake Up Safe Quality Improvement Initiative.

PubMed

Lobaugh, Lauren M Y; Martin, Lizabeth D; Schleelein, Laura E; Tyler, Donald C; Litman, Ronald S

2017-09-01

Wake Up Safe is a quality improvement initiative of the Society for Pediatric Anesthesia that contains a deidentified registry of serious adverse events occurring in pediatric anesthesia. The aim of this study was to describe and characterize reported medication errors to find common patterns amenable to preventative strategies. In September 2016, we analyzed approximately 6 years' worth of medication error events reported to Wake Up Safe. Medication errors were classified by: (1) medication category; (2) error type by phase of administration: prescribing, preparation, or administration; (3) bolus or infusion error; (4) provider type and level of training; (5) harm as defined by the National Coordinating Council for Medication Error Reporting and Prevention; and (6) perceived preventability. From 2010 to the time of our data analysis in September 2016, 32 institutions had joined and submitted data on 2087 adverse events during 2,316,635 anesthetics. These reports contained details of 276 medication errors, which comprised the third highest category of events behind cardiac and respiratory related events. Medication errors most commonly involved opioids and sedative/hypnotics. When categorized by phase of handling, 30 events occurred during preparation, 67 during prescribing, and 179 during administration. The most common error type was accidental administration of the wrong dose (N = 84), followed by syringe swap (accidental administration of the wrong syringe, N = 49). Fifty-seven (21%) reported medication errors involved medications prepared as infusions as opposed to 1 time bolus administrations. Medication errors were committed by all types of anesthesia providers, most commonly by attendings. Over 80% of reported medication errors reached the patient and more than half of these events caused patient harm. Fifteen events (5%) required a life sustaining intervention. Nearly all cases (97%) were judged to be either likely or certainly preventable. Our findings characterize the most common types of medication errors in pediatric anesthesia practice and provide guidance on future preventative strategies. Many of these errors will be almost entirely preventable with the use of prefilled medication syringes to avoid accidental ampule swap, bar-coding at the point of medication administration to prevent syringe swap and to confirm the proper dose, and 2-person checking of medication infusions for accuracy.

Typing mineral deposits using their grades and tonnages in an artificial neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

2003-01-01

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.

Common component classification: what can we learn from machine learning?

PubMed

Anderson, Ariana; Labus, Jennifer S; Vianna, Eduardo P; Mayer, Emeran A; Cohen, Mark S

2011-05-15

Machine learning methods have been applied to classifying fMRI scans by studying locations in the brain that exhibit temporal intensity variation between groups, frequently reporting classification accuracy of 90% or better. Although empirical results are quite favorable, one might doubt the ability of classification methods to withstand changes in task ordering and the reproducibility of activation patterns over runs, and question how much of the classification machines' power is due to artifactual noise versus genuine neurological signal. To examine the true strength and power of machine learning classifiers we create and then deconstruct a classifier to examine its sensitivity to physiological noise, task reordering, and across-scan classification ability. The models are trained and tested both within and across runs to assess stability and reproducibility across conditions. We demonstrate the use of independent components analysis for both feature extraction and artifact removal and show that removal of such artifacts can reduce predictive accuracy even when data has been cleaned in the preprocessing stages. We demonstrate how mistakes in the feature selection process can cause the cross-validation error seen in publication to be a biased estimate of the testing error seen in practice and measure this bias by purposefully making flawed models. We discuss other ways to introduce bias and the statistical assumptions lying behind the data and model themselves. Finally we discuss the complications in drawing inference from the smaller sample sizes typically seen in fMRI studies, the effects of small or unbalanced samples on the Type 1 and Type 2 error rates, and how publication bias can give a false confidence of the power of such methods. Collectively this work identifies challenges specific to fMRI classification and methods affecting the stability of models. Copyright © 2010 Elsevier Inc. All rights reserved.

Echinocandin Susceptibility Testing of Candida Species: Comparison of EUCAST EDef 7.1, CLSI M27-A3, Etest, Disk Diffusion, and Agar Dilution Methods with RPMI and IsoSensitest Media▿

PubMed Central

Arendrup, Maiken Cavling; Garcia-Effron, Guillermo; Lass-Flörl, Cornelia; Lopez, Alicia Gomez; Rodriguez-Tudela, Juan-Luis; Cuenca-Estrella, Manuel; Perlin, David S.

2010-01-01

This study compared nine susceptibility testing methods and 12 endpoints for anidulafungin, caspofungin, and micafungin with the same collection of blinded FKS hot spot mutant (n = 29) and wild-type isolates (n = 94). The susceptibility tests included EUCAST Edef 7.1, agar dilution, Etest, and disk diffusion with RPMI-1640 plus 2% glucose (2G) and IsoSensitest-2G media and CLSI M27A-3. Microdilution plates were read after 24 and 48 h. The following test parameters were evaluated: fks hot spot mutants overlapping the wild-type distribution, distance between the two populations, number of very major errors (VMEs; fks mutants misclassified as susceptible), and major errors (MEs; wild-type isolates classified as resistant) using a wild-type-upper-limit value (WT-UL) (two twofold-dilutions higher than the MIC50) as the susceptibility breakpoint. The methods with the lowest number of errors (given as VMEs/MEs) across the three echinocandins were CLSI (12%/1%), agar dilution with RPMI-2G medium (14%/0%), and Etest with RPMI-2G medium (8%/3%). The fewest errors overall were observed for anidulafungin (4%/1% for EUCAST, 4%/3% for CLSI, and 3%/9% for Etest with RPMI-2G). For micafungin, VME rates of 10 to 71% were observed. For caspofungin, agar dilution with either medium was superior (VMEs/MEs of 0%/1%), while CLSI, EUCAST with IsoSensitest-2G medium, and Etest were less optimal (VMEs of 7%, 10%, and 10%, respectively). Applying the CLSI breakpoint (S ≤ 2 μg/ml) for CLSI results, 89.2% fks hot spot mutants were classified as anidulafungin susceptible, 60.7% as caspofungin susceptible, and 92.9% as micafungin susceptible. In conclusion, no test was perfect, but anidulafungin susceptibility testing using the WT-UL to define susceptibility reliably identified fks hot spot mutants. PMID:19884370

Bayes Error Rate Estimation Using Classifier Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2003-01-01

The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.

Comparison of wheat classification accuracy using different classifiers of the image-100 system

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.

1981-01-01

Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.

Microscopic saw mark analysis: an empirical approach.

PubMed

Love, Jennifer C; Derrick, Sharon M; Wiersema, Jason M; Peters, Charles

2015-01-01

Microscopic saw mark analysis is a well published and generally accepted qualitative analytical method. However, little research has focused on identifying and mitigating potential sources of error associated with the method. The presented study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw mark analysis. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82%. © 2014 American Academy of Forensic Sciences.

A Framework for Identifying and Classifying Undergraduate Student Proof Errors

ERIC Educational Resources Information Center

Strickland, S.; Rand, B.

2016-01-01

This paper describes a framework for identifying, classifying, and coding student proofs, modified from existing proof-grading rubrics. The framework includes 20 common errors, as well as categories for interpreting the severity of the error. The coding scheme is intended for use in a classroom context, for providing effective student feedback. In…

Prepopulated radiology report templates: a prospective analysis of error rate and turnaround time.

PubMed

Hawkins, C M; Hall, S; Hardin, J; Salisbury, S; Towbin, A J

2012-08-01

Current speech recognition software allows exam-specific standard reports to be prepopulated into the dictation field based on the radiology information system procedure code. While it is thought that prepopulating reports can decrease the time required to dictate a study and the overall number of errors in the final report, this hypothesis has not been studied in a clinical setting. A prospective study was performed. During the first week, radiologists dictated all studies using prepopulated standard reports. During the second week, all studies were dictated after prepopulated reports had been disabled. Final radiology reports were evaluated for 11 different types of errors. Each error within a report was classified individually. The median time required to dictate an exam was compared between the 2 weeks. There were 12,387 reports dictated during the study, of which, 1,173 randomly distributed reports were analyzed for errors. There was no difference in the number of errors per report between the 2 weeks; however, radiologists overwhelmingly preferred using a standard report both weeks. Grammatical errors were by far the most common error type, followed by missense errors and errors of omission. There was no significant difference in the median dictation time when comparing studies performed each week. The use of prepopulated reports does not alone affect the error rate or dictation time of radiology reports. While it is a useful feature for radiologists, it must be coupled with other strategies in order to decrease errors.

Errors detected in pediatric oral liquid medication doses prepared in an automated workflow management system.

PubMed

Bledsoe, Sarah; Van Buskirk, Alex; Falconer, R James; Hollon, Andrew; Hoebing, Wendy; Jokic, Sladan

2018-02-01

The effectiveness of barcode-assisted medication preparation (BCMP) technology on detecting oral liquid dose preparation errors. From June 1, 2013, through May 31, 2014, a total of 178,344 oral doses were processed at Children's Mercy, a 301-bed pediatric hospital, through an automated workflow management system. Doses containing errors detected by the system's barcode scanning system or classified as rejected by the pharmacist were further reviewed. Errors intercepted by the barcode-scanning system were classified as (1) expired product, (2) incorrect drug, (3) incorrect concentration, and (4) technological error. Pharmacist-rejected doses were categorized into 6 categories based on the root cause of the preparation error: (1) expired product, (2) incorrect concentration, (3) incorrect drug, (4) incorrect volume, (5) preparation error, and (6) other. Of the 178,344 doses examined, 3,812 (2.1%) errors were detected by either the barcode-assisted scanning system (1.8%, n = 3,291) or a pharmacist (0.3%, n = 521). The 3,291 errors prevented by the barcode-assisted system were classified most commonly as technological error and incorrect drug, followed by incorrect concentration and expired product. Errors detected by pharmacists were also analyzed. These 521 errors were most often classified as incorrect volume, preparation error, expired product, other, incorrect drug, and incorrect concentration. BCMP technology detected errors in 1.8% of pediatric oral liquid medication doses prepared in an automated workflow management system, with errors being most commonly attributed to technological problems or incorrect drugs. Pharmacists rejected an additional 0.3% of studied doses. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Convolutional neural network with transfer learning for rice type classification

NASA Astrophysics Data System (ADS)

Patel, Vaibhav Amit; Joshi, Manjunath V.

2018-04-01

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

Authentication of the botanical origin of honey by near-infrared spectroscopy.

PubMed

Ruoff, Kaspar; Luginbühl, Werner; Bogdanov, Stefan; Bosset, Jacques Olivier; Estermann, Barbara; Ziolko, Thomas; Amado, Renato

2006-09-06

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.

[Professional misconduct in obstetrics and gynecology in light of the Supreme Medical Court between 2002-2012].

PubMed

Kordel, Piotr; Kordel, Krzysztof

2014-11-01

The aim of the study was to present and analyze the verdicts of the Supreme Medical Court concerning professional misconduct among obstetrics and gynecology specialists between 2002-2012. Verdicts of the Supreme Medical Court from 84 cases concerning obstetrics and gynecology speciallsts, passed between 2002-20 12, were analyzed. The following categories were used to classify the types of professional misconduct: decisive erro, error in the performance of a medical procedure, organizational errol error of professional judgment, criminal offence, and unethical behavior. The largest group among the accused professionals were doctors working in private offices and on-call doctors in urban and district hospitals. The most frequent type of professional malpractice was decisive error and the most frequent type of case were obstetric labor complications. The analysis also showed a correlation between the type of case and the sentence in the Supreme Medical Court. A respective jurisdiction approach may be observed in the Supreme Medical Court ruling against cases concerning professional misconduct which are also criminal offences (i.e., illegal abortion, working under the influence). The most frequent types of professional misconduct should determine areas for professional training of obstetrics and gynecology specialists.

Assessment of Nonverbal and Verbal Apraxia in Patients with Parkinson's Disease

PubMed Central

Olchik, Maira Rozenfeld; Shumacher Shuh, Artur Francisco; Rieder, Carlos R. M.

2015-01-01

Objective. To assess the presence of nonverbal and verbal apraxia in patients with Parkinson's disease (PD) and analyze the correlation between these conditions and patient age, education, duration of disease, and PD stage, as well as evaluate the correlation between the two types of apraxia and the frequency and types of verbal apraxic errors made by patients in the sample. Method. This was an observational prevalence study. The sample comprised 45 patients with PD seen at the Movement Disorders Clinic of the Clinical Hospital of Porto Alegre, Brazil. Patients were evaluated using the Speech Apraxia Assessment Protocol and PD stages were classified according to the Hoehn and Yahr scale. Results. The rate of nonverbal apraxia and verbal apraxia in the present sample was 24.4%. Verbal apraxia was significantly correlated with education (p ≤ 0.05). The most frequent types of verbal apraxic errors were omissions (70.8%). The analysis of manner and place of articulation showed that most errors occurred during the production of trill (57.7%) and dentoalveolar (92%) phonemes, consecutively. Conclusion. Patients with PD presented nonverbal and verbal apraxia and made several verbal apraxic errors. Verbal apraxia was correlated with education levels. PMID:26543663

Retracted publications in the drug literature.

PubMed

Samp, Jennifer C; Schumock, Glen T; Pickard, A Simon

2012-07-01

Recent studies have suggested an increase in the number of retracted scientific publications. It is unclear how broadly the issue of misleading and fraudulent publications pertains to retractions of drug therapy studies. Therefore, we sought to determine the trends and factors associated with retracted publications in drug therapy literature. A PubMed search was conducted to identify retracted drug therapy articles published from 2000-2011. Articles were grouped according to reason for retraction, which was classified as scientific misconduct or error. Scientific misconduct was further divided into data fabrication, data falsification, questions of data veracity, unethical author conduct, and plagiarism. Error was defined as duplicate publication, scientific mistake, journal error, or unstated reasons. Additional data were extracted from the retracted articles, including type of article, funding source, author information, therapeutic area, and retraction issue. A total of 742 retractions were identified from 2000-2011 in the general biomedical literature, and 102 drug studies met our inclusion criteria. Of these, 73 articles (72%) were retracted for a reason classified as scientific misconduct, whereas 29 articles (28%) were retracted for error. Among the 73 articles classified as scientific misconduct, those classified as unethical author conduct (32 articles [44%]) and data fabrication (24 articles [33%]) constituted the majority. The median time from publication of the original article to retraction was 31 months (range 1-130). Fifty percent of retracted articles did not state a funding source, whereas pharmaceutical manufacturer funding accounted for only 13 articles (13%) analyzed. Many retractions were due to repeat offenses by a small number of authors, with nearly 40% of the retracted studies associated with two individuals. We found that a greater proportion of drug therapy articles were retracted for reasons of misconduct and fraud compared with other biomedical studies. It is important for health care practitioners to monitor the literature for retractions so that recommendations for drug therapy and patient management may be modified accordingly. © 2012 Pharmacotherapy Publications, Inc. All rights reserved.

Relative Proportion Of Different Types Of Refractive Errors In Subjects Seeking Laser Vision Correction.

PubMed

Althomali, Talal A

2018-01-01

Refractive errors are a form of optical defect affecting more than 2.3 billion people worldwide. As refractive errors are a major contributor of mild to moderate vision impairment, assessment of their relative proportion would be helpful in the strategic planning of health programs. To determine the pattern of the relative proportion of types of refractive errors among the adult candidates seeking laser assisted refractive correction in a private clinic setting in Saudi Arabia. The clinical charts of 687 patients (1374 eyes) with mean age 27.6 ± 7.5 years who desired laser vision correction and underwent a pre-LASIK work-up were reviewed retrospectively. Refractive errors were classified as myopia, hyperopia and astigmatism. Manifest refraction spherical equivalent (MRSE) was applied to define refractive errors. Distribution percentage of different types of refractive errors; myopia, hyperopia and astigmatism. The mean spherical equivalent for 1374 eyes was -3.11 ± 2.88 D. Of the total 1374 eyes, 91.8% (n = 1262) eyes had myopia, 4.7% (n = 65) eyes had hyperopia and 3.4% (n = 47) had emmetropia with astigmatism. Distribution percentage of astigmatism (cylinder error of ≥ 0.50 D) was 78.5% (1078/1374 eyes); of which % 69.1% (994/1374) had low to moderate astigmatism and 9.4% (129/1374) had high astigmatism. Of the adult candidates seeking laser refractive correction in a private setting in Saudi Arabia, myopia represented greatest burden with more than 90% myopic eyes, compared to hyperopia in nearly 5% eyes. Astigmatism was present in more than 78% eyes.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Anatomic, clinical, and neuropsychological correlates of spelling errors in primary progressive aphasia.

PubMed

Shim, Hyungsub; Hurley, Robert S; Rogalski, Emily; Mesulam, M-Marsel

2012-07-01

This study evaluates spelling errors in the three subtypes of primary progressive aphasia (PPA): agrammatic (PPA-G), logopenic (PPA-L), and semantic (PPA-S). Forty-one PPA patients and 36 age-matched healthy controls were administered a test of spelling. The total number of errors and types of errors in spelling to dictation of regular words, exception words and nonwords, were recorded. Error types were classified based on phonetic plausibility. In the first analysis, scores were evaluated by clinical diagnosis. Errors in spelling exception words and phonetically plausible errors were seen in PPA-S. Conversely, PPA-G was associated with errors in nonword spelling and phonetically implausible errors. In the next analysis, spelling scores were correlated to other neuropsychological language test scores. Significant correlations were found between exception word spelling and measures of naming and single word comprehension. Nonword spelling correlated with tests of grammar and repetition. Global language measures did not correlate significantly with spelling scores, however. Cortical thickness analysis based on MRI showed that atrophy in several language regions of interest were correlated with spelling errors. Atrophy in the left supramarginal gyrus and inferior frontal gyrus (IFG) pars orbitalis correlated with errors in nonword spelling, while thinning in the left temporal pole and fusiform gyrus correlated with errors in exception word spelling. Additionally, phonetically implausible errors in regular word spelling correlated with thinning in the left IFG pars triangularis and pars opercularis. Together, these findings suggest two independent systems for spelling to dictation, one phonetic (phoneme to grapheme conversion), and one lexical (whole word retrieval). Copyright © 2012 Elsevier Ltd. All rights reserved.

A novel ETFB mutation in a patient with glutaric aciduria type II.

PubMed

Sudo, Yosuke; Sasaki, Ayako; Wakabayashi, Takashi; Numakura, Chikahiko; Hayasaka, Kiyoshi

2015-01-01

Glutaric aciduria type II (GAII) is a rare inborn error of metabolism clinically classified into a neonatal-onset form with congenital anomalies, a neonatal-onset form without congenital anomalies and a mild and/or late-onset form (MIM #231680). Here, we report on a GAII patient carrying a homozygous novel c.143_145delAGG (p.Glu48del) mutation in the ETFB gene, who presented with a neonatal-onset form with congenital anomalies and rapidly developed cardiomegaly after birth.

A novel ETFB mutation in a patient with glutaric aciduria type II

PubMed Central

Sudo, Yosuke; Sasaki, Ayako; Wakabayashi, Takashi; Numakura, Chikahiko; Hayasaka, Kiyoshi

2015-01-01

Glutaric aciduria type II (GAII) is a rare inborn error of metabolism clinically classified into a neonatal-onset form with congenital anomalies, a neonatal-onset form without congenital anomalies and a mild and/or late-onset form (MIM #231680). Here, we report on a GAII patient carrying a homozygous novel c.143_145delAGG (p.Glu48del) mutation in the ETFB gene, who presented with a neonatal-onset form with congenital anomalies and rapidly developed cardiomegaly after birth. PMID:27081516

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

Definition of an Acceptable Glass composition Region (AGCR) via an Index System and a Partitioning Function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peeler, D. K.; Taylor, A. S.; Edwards, T.B.

2005-06-26

The objective of this investigation was to appeal to the available ComPro{trademark} database of glass compositions and measured PCTs that have been generated in the study of High Level Waste (HLW)/Low Activity Waste (LAW) glasses to define an Acceptable Glass Composition Region (AGCR). The term AGCR refers to a glass composition region in which the durability response (as defined by the Product Consistency Test (PCT)) is less than some pre-defined, acceptable value that satisfies the Waste Acceptance Product Specifications (WAPS)--a value of 10 g/L was selected for this study. To assess the effectiveness of a specific classification or index systemmore » to differentiate between acceptable and unacceptable glasses, two types of errors (Type I and Type II errors) were monitored. A Type I error reflects that a glass with an acceptable durability response (i.e., a measured NL [B] < 10 g/L) is classified as unacceptable by the system of composition-based constraints. A Type II error occurs when a glass with an unacceptable durability response is classified as acceptable by the system of constraints. Over the course of the efforts to meet this objective, two approaches were assessed. The first (referred to as the ''Index System'') was based on the use of an evolving system of compositional constraints which were used to explore the possibility of defining an AGCR. This approach was primarily based on ''glass science'' insight to establish the compositional constraints. Assessments of the Brewer and Taylor Index Systems did not result in the definition of an AGCR. Although the Taylor Index System minimized Type I errors which allowed access to composition regions of interest to improve melt rate or increase waste loadings for DWPF as compared to the current durability model, Type II errors were also committed. In the context of the application of a particular classification system in the process control system, Type II errors are much more serious than Type I errors. A Type I error only reflects that the particular constraint system being used is overly conservative (i.e., its application restricts access to glasses that have an acceptable measured durability response). A Type II error results in a more serious misclassification that could result in allowing the transfer of a Slurry Mix Evaporator (SME) batch to the melter, which is predicted to produce a durable product based on the specific system applied but in reality does not meet the defined ''acceptability'' criteria. More specifically, a nondurable product could be produced in DWPF. Given the presence of Type II errors, the Index System approach was deemed inadequate for further implementation consideration at the DWPF. The second approach (the JMP partitioning process) was purely data driven and empirically derived--glass science was not a factor. In this approach, the collection of composition--durability data in ComPro was sequentially partitioned or split based on the best available specific criteria and variables. More specifically, the JMP software chose the oxide (Al{sub 2}O{sub 3} for this dataset) that most effectively partitions the PCT responses (NL [B]'s)--perhaps not 100% effective based on a single oxide. Based on this initial split, a second request was made to split a particular set of the ''Y'' values (good or bad PCTs based on the 10 g/L limit) based on the next most critical ''X'' variable. This ''splitting'' or ''partitioning'' process was repeated until an AGCR was defined based on the use of only 3 oxides (Al{sub 2}O{sub 3}, CaO, and MgO) and critical values of > 3.75 wt% Al{sub 2}O{sub 3}, {ge} 0.616 wt% CaO, and < 3.521 wt% MgO. Using this set of criteria, the ComPro database was partitioned in which no Type II errors were committed. The automated partitioning function screened or removed 978 of the 2406 ComPro glasses which did cause some initial concerns regarding excessive conservatism regardless of its ability to identify an AGCR. However, a preliminary review of glasses within the 1428 ''acceptable'' glasses defining the ACGR includes glass systems of interest to support the accelerated mission.« less

A classification on human factor accident/incident of China civil aviation in recent twelve years.

PubMed

Luo, Xiao-li

2004-10-01

To study human factor accident/incident occurred during 1990-2001 using new classification standard. The human factor accident/incident classification standard is developed on the basis of Reason's Model, combining with CAAC's traditional classifying method, and applied to the classified statistical analysis for 361 flying incidents and 35 flight accidents of China civil aviation, which is induced by human factors and occurred from 1990 to 2001. 1) the incident percentage of taxi and cruise is higher than that of takeoff, climb and descent. 2) The dominating type of flight incidents is diverging of runway, overrunning, near-miss, tail/wingtip/engine strike and ground obstacle impacting. 3) The top three accidents are out of control caused by crew, mountain collision and over runway. 4) Crew's basic operating skill is lower than what we imagined, the mostly representation is poor correcting ability when flight error happened. 5) Crew errors can be represented by incorrect control, regulation and procedure violation, disorientation and diverging percentage of correct flight level. The poor CRM skill is the dominant factor impacting China civil aviation safety, this result has a coincidence with previous study, but there is much difference and distinct characteristic in top incident phase, the type of crew error and behavior performance compared with that of advanced countries. We should strengthen CRM training for all of pilots aiming at the Chinese pilot behavior characteristic in order to improve the safety level of China civil aviation.

The role of model errors represented by nonlinear forcing singular vector tendency error in causing the "spring predictability barrier" within ENSO predictions

NASA Astrophysics Data System (ADS)

Duan, Wansuo; Zhao, Peng

2017-04-01

Within the Zebiak-Cane model, the nonlinear forcing singular vector (NFSV) approach is used to investigate the role of model errors in the "Spring Predictability Barrier" (SPB) phenomenon within ENSO predictions. NFSV-related errors have the largest negative effect on the uncertainties of El Niño predictions. NFSV errors can be classified into two types: the first is characterized by a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central-western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite the first type. The first type of error tends to have the worst effects on El Niño growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV-related errors exhibits prominent seasonality, with the fastest error growth in the spring and/or summer seasons; hence, these errors result in a significant SPB related to El Niño events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate a SPB for El Niño events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Niño events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Niño predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.

Review of medication errors that are new or likely to occur more frequently with electronic medication management systems.

PubMed

Van de Vreede, Melita; McGrath, Anne; de Clifford, Jan

2018-05-14

Objective. The aim of the present study was to identify and quantify medication errors reportedly related to electronic medication management systems (eMMS) and those considered likely to occur more frequently with eMMS. This included developing a new classification system relevant to eMMS errors. Methods. Eight Victorian hospitals with eMMS participated in a retrospective audit of reported medication incidents from their incident reporting databases between May and July 2014. Site-appointed project officers submitted deidentified incidents they deemed new or likely to occur more frequently due to eMMS, together with the Incident Severity Rating (ISR). The authors reviewed and classified incidents. Results. There were 5826 medication-related incidents reported. In total, 93 (47 prescribing errors, 46 administration errors) were identified as new or potentially related to eMMS. Only one ISR2 (moderate) and no ISR1 (severe or death) errors were reported, so harm to patients in this 3-month period was minimal. The most commonly reported error types were 'human factors' and 'unfamiliarity or training' (70%) and 'cross-encounter or hybrid system errors' (22%). Conclusions. Although the results suggest that the errors reported were of low severity, organisations must remain vigilant to the risk of new errors and avoid the assumption that eMMS is the panacea to all medication error issues. What is known about the topic? eMMS have been shown to reduce some types of medication errors, but it has been reported that some new medication errors have been identified and some are likely to occur more frequently with eMMS. There are few published Australian studies that have reported on medication error types that are likely to occur more frequently with eMMS in more than one organisation and that include administration and prescribing errors. What does this paper add? This paper includes a new simple classification system for eMMS that is useful and outlines the most commonly reported incident types and can inform organisations and vendors on possible eMMS improvements. The paper suggests a new classification system for eMMS medication errors. What are the implications for practitioners? The results of the present study will highlight to organisations the need for ongoing review of system design, refinement of workflow issues, staff education and training and reporting and monitoring of errors.

How common are cognitive errors in cases presented at emergency medicine resident morbidity and mortality conferences?

PubMed

Chu, David; Xiao, Jane; Shah, Payal; Todd, Brett

2018-06-20

Cognitive errors are a major contributor to medical error. Traditionally, medical errors at teaching hospitals are analyzed in morbidity and mortality (M&M) conferences. We aimed to describe the frequency of cognitive errors in relation to the occurrence of diagnostic and other error types, in cases presented at an emergency medicine (EM) resident M&M conference. We conducted a retrospective study of all cases presented at a suburban US EM residency monthly M&M conference from September 2011 to August 2016. Each case was reviewed using the electronic medical record (EMR) and notes from the M&M case by two EM physicians. Each case was categorized by type of primary medical error that occurred as described by Okafor et al. When a diagnostic error occurred, the case was reviewed for contributing cognitive and non-cognitive factors. Finally, when a cognitive error occurred, the case was classified into faulty knowledge, faulty data gathering or faulty synthesis, as described by Graber et al. Disagreements in error type were mediated by a third EM physician. A total of 87 M&M cases were reviewed; the two reviewers agreed on 73 cases, and 14 cases required mediation by a third reviewer. Forty-eight cases involved diagnostic errors, 47 of which were cognitive errors. Of these 47 cases, 38 involved faulty synthesis, 22 involved faulty data gathering and only 11 involved faulty knowledge. Twenty cases contained more than one type of cognitive error. Twenty-nine cases involved both a resident and an attending physician, while 17 cases involved only an attending physician. Twenty-one percent of the resident cases involved all three cognitive errors, while none of the attending cases involved all three. Forty-one percent of the resident cases and only 6% of the attending cases involved faulty knowledge. One hundred percent of the resident cases and 94% of the attending cases involved faulty synthesis. Our review of 87 EM M&M cases revealed that cognitive errors are commonly involved in cases presented, and that these errors are less likely due to deficient knowledge and more likely due to faulty synthesis. M&M conferences may therefore provide an excellent forum to discuss cognitive errors and how to reduce their occurrence.

Teaching Grammar: The Use of The English Auxiliary "BE" Present Tense Verb among Malaysian Form 4 and Form 5 Students

ERIC Educational Resources Information Center

Jishvithaa, Joanna M.; Tabitha, M.; Kalajahi, Seyed Ali Rezvani

2013-01-01

This research paper aims to explore the usage of the English Auxiliary "Be" Present Tense Verb, using corpus based method among Malaysian form 4 and form 5 students. This study is conducted by identifying and classifying the types of errors in the Auxiliary "Be" Present Tense verb in students' compositions from the MCSAW corpus…

Single Event Effect Testing of the Analog Devices ADV212

NASA Technical Reports Server (NTRS)

Wilcox, Ted; Campola, Michael; Kadari, Madhu; Nadendla, Seshagiri R.

2017-01-01

The Analog Devices ADV212 was initially tested for single event effects (SEE) at the Texas AM University Cyclotron Facility (TAMU) in July of 2013. Testing revealed a sensitivity to device hang-ups classified as single event functional interrupts (SEFI), soft data errors classified as single event upsets (SEU), and, of particular concern, single event latch-ups (SEL). All error types occurred so frequently as to make accurate measurements of the exposure time, and thus total particle fluence, challenging. To mitigate some of the risk posed by single event latch-ups, circuitry was added to the electrical design to detect a high current event and automatically recycle power and reboot the device. An additional heavy-ion test was scheduled to validate the operation of the recovery circuitry and the continuing functionality of the ADV212 after a substantial number of latch-up events. As a secondary goal, more precise data would be gathered by an improved test method, described in this test report.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

PubMed

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Relative Proportion Of Different Types Of Refractive Errors In Subjects Seeking Laser Vision Correction

PubMed Central

Althomali, Talal A.

2018-01-01

Background: Refractive errors are a form of optical defect affecting more than 2.3 billion people worldwide. As refractive errors are a major contributor of mild to moderate vision impairment, assessment of their relative proportion would be helpful in the strategic planning of health programs. Purpose: To determine the pattern of the relative proportion of types of refractive errors among the adult candidates seeking laser assisted refractive correction in a private clinic setting in Saudi Arabia. Methods: The clinical charts of 687 patients (1374 eyes) with mean age 27.6 ± 7.5 years who desired laser vision correction and underwent a pre-LASIK work-up were reviewed retrospectively. Refractive errors were classified as myopia, hyperopia and astigmatism. Manifest refraction spherical equivalent (MRSE) was applied to define refractive errors. Outcome Measures: Distribution percentage of different types of refractive errors; myopia, hyperopia and astigmatism. Results: The mean spherical equivalent for 1374 eyes was -3.11 ± 2.88 D. Of the total 1374 eyes, 91.8% (n = 1262) eyes had myopia, 4.7% (n = 65) eyes had hyperopia and 3.4% (n = 47) had emmetropia with astigmatism. Distribution percentage of astigmatism (cylinder error of ≥ 0.50 D) was 78.5% (1078/1374 eyes); of which % 69.1% (994/1374) had low to moderate astigmatism and 9.4% (129/1374) had high astigmatism. Conclusion and Relevance: Of the adult candidates seeking laser refractive correction in a private setting in Saudi Arabia, myopia represented greatest burden with more than 90% myopic eyes, compared to hyperopia in nearly 5% eyes. Astigmatism was present in more than 78% eyes. PMID:29872484

Exception handling for sensor fusion

NASA Astrophysics Data System (ADS)

Chavez, G. T.; Murphy, Robin R.

1993-08-01

This paper presents a control scheme for handling sensing failures (sensor malfunctions, significant degradations in performance due to changes in the environment, and errant expectations) in sensor fusion for autonomous mobile robots. The advantages of the exception handling mechanism are that it emphasizes a fast response to sensing failures, is able to use only a partial causal model of sensing failure, and leads to a graceful degradation of sensing if the sensing failure cannot be compensated for. The exception handling mechanism consists of two modules: error classification and error recovery. The error classification module in the exception handler attempts to classify the type and source(s) of the error using a modified generate-and-test procedure. If the source of the error is isolated, the error recovery module examines its cache of recovery schemes, which either repair or replace the current sensing configuration. If the failure is due to an error in expectation or cannot be identified, the planner is alerted. Experiments using actual sensor data collected by the CSM Mobile Robotics/Machine Perception Laboratory's Denning mobile robot demonstrate the operation of the exception handling mechanism.

Human errors and violations in computer and information security: the viewpoint of network administrators and security specialists.

PubMed

Kraemer, Sara; Carayon, Pascale

2007-03-01

This paper describes human errors and violations of end users and network administration in computer and information security. This information is summarized in a conceptual framework for examining the human and organizational factors contributing to computer and information security. This framework includes human error taxonomies to describe the work conditions that contribute adversely to computer and information security, i.e. to security vulnerabilities and breaches. The issue of human error and violation in computer and information security was explored through a series of 16 interviews with network administrators and security specialists. The interviews were audio taped, transcribed, and analyzed by coding specific themes in a node structure. The result is an expanded framework that classifies types of human error and identifies specific human and organizational factors that contribute to computer and information security. Network administrators tended to view errors created by end users as more intentional than unintentional, while errors created by network administrators as more unintentional than intentional. Organizational factors, such as communication, security culture, policy, and organizational structure, were the most frequently cited factors associated with computer and information security.

Development and validation of Aviation Causal Contributors for Error Reporting Systems (ACCERS).

PubMed

Baker, David P; Krokos, Kelley J

2007-04-01

This investigation sought to develop a reliable and valid classification system for identifying and classifying the underlying causes of pilot errors reported under the Aviation Safety Action Program (ASAP). ASAP is a voluntary safety program that air carriers may establish to study pilot and crew performance on the line. In ASAP programs, similar to the Aviation Safety Reporting System, pilots self-report incidents by filing a short text description of the event. The identification of contributors to errors is critical if organizations are to improve human performance, yet it is difficult for analysts to extract this information from text narratives. A taxonomy was needed that could be used by pilots to classify the causes of errors. After completing a thorough literature review, pilot interviews and a card-sorting task were conducted in Studies 1 and 2 to develop the initial structure of the Aviation Causal Contributors for Event Reporting Systems (ACCERS) taxonomy. The reliability and utility of ACCERS was then tested in studies 3a and 3b by having pilots independently classify the primary and secondary causes of ASAP reports. The results provided initial evidence for the internal and external validity of ACCERS. Pilots were found to demonstrate adequate levels of agreement with respect to their category classifications. ACCERS appears to be a useful system for studying human error captured under pilot ASAP reports. Future work should focus on how ACCERS is organized and whether it can be used or modified to classify human error in ASAP programs for other aviation-related job categories such as dispatchers. Potential applications of this research include systems in which individuals self-report errors and that attempt to extract and classify the causes of those events.

Human Reliability and the Cost of Doing Business

NASA Technical Reports Server (NTRS)

DeMott, Diana

2014-01-01

Most businesses recognize that people will make mistakes and assume errors are just part of the cost of doing business, but does it need to be? Companies with high risk, or major consequences, should consider the effect of human error. In a variety of industries, Human Errors have caused costly failures and workplace injuries. These have included: airline mishaps, medical malpractice, administration of medication and major oil spills have all been blamed on human error. A technique to mitigate or even eliminate some of these costly human errors is the use of Human Reliability Analysis (HRA). Various methodologies are available to perform Human Reliability Assessments that range from identifying the most likely areas for concern to detailed assessments with human error failure probabilities calculated. Which methodology to use would be based on a variety of factors that would include: 1) how people react and act in different industries, and differing expectations based on industries standards, 2) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 3) type and availability of data and 4) how the industry views risk & reliability influences ( types of emergencies, contingencies and routine tasks versus cost based concerns). The Human Reliability Assessments should be the first step to reduce, mitigate or eliminate the costly mistakes or catastrophic failures. Using Human Reliability techniques to identify and classify human error risks allows a company more opportunities to mitigate or eliminate these risks and prevent costly failures.

Prescribing errors in adult congenital heart disease patients admitted to a pediatric cardiovascular intensive care unit.

PubMed

Echeta, Genevieve; Moffett, Brady S; Checchia, Paul; Benton, Mary Kay; Klouda, Leda; Rodriguez, Fred H; Franklin, Wayne

2014-01-01

Adults with congenital heart disease (CHD) are often cared for at pediatric hospitals. There are no data describing the incidence or type of medication prescribing errors in adult patients admitted to a pediatric cardiovascular intensive care unit (CVICU). A review of patients >18 years of age admitted to the pediatric CVICU at our institution from 2009 to 2011 occurred. A comparator group <18 years of age but >70 kg (a typical adult weight) was identified. Medication prescribing errors were determined according to a commonly used adult drug reference. An independent panel consisting of a physician specializing in the care of adult CHD patients, a nurse, and a pharmacist evaluated all errors. Medication prescribing orders were classified as appropriate, underdose, overdose, or nonstandard (dosing per weight instead of standard adult dosing), and severity of error was classified. Eighty-five adult (74 patients) and 33 pediatric admissions (32 patients) met study criteria (mean age 27.5 ± 9.4 years, 53% male vs. 14.9 ± 1.8 years, 63% male). A cardiothoracic surgical procedure occurred in 81.4% of admissions. Adult admissions weighed less than pediatric admissions (72.8 ± 22.4 kg vs. 85.6 ± 14.9 kg, P < .01) but hospital length of stay was similar. (Adult 6 days [range 1-216 days]; pediatric 5 days [Range 2-123 days], P = .52.) A total of 112 prescribing errors were identified and they occurred less often in adults (42.4% of admissions vs. 66.7% of admissions, P = .02). Adults had a lower mean number of errors (0.7 errors per adult admission vs. 1.7 errors per pediatric admission, P < .01). Prescribing errors occurred most commonly with antimicrobials (n = 27). Underdosing was the most common category of prescribing error. Most prescribing errors were determined to have not caused harm to the patient. Prescribing errors occur frequently in adult patients admitted to a pediatric CVICU but occur more often in pediatric patients of adult weight. © 2013 Wiley Periodicals, Inc.

Moments and Root-Mean-Square Error of the Bayesian MMSE Estimator of Classification Error in the Gaussian Model.

PubMed

Zollanvari, Amin; Dougherty, Edward R

2014-06-01

The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

PubMed

Silva Filho, Telmo M; Souza, Renata M C R; Prudêncio, Ricardo B C

2016-08-01

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

On the decoding process in ternary error-correcting output codes.

PubMed

Escalera, Sergio; Pujol, Oriol; Radeva, Petia

2010-01-01

A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-Correcting Output Codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a "do not care" symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI Machine Learning Repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved.

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

An error taxonomy system for analysis of haemodialysis incidents.

PubMed

Gu, Xiuzhu; Itoh, Kenji; Suzuki, Satoshi

2014-12-01

This paper describes the development of a haemodialysis error taxonomy system for analysing incidents and predicting the safety status of a dialysis organisation. The error taxonomy system was developed by adapting an error taxonomy system which assumed no specific specialty to haemodialysis situations. Its application was conducted with 1,909 incident reports collected from two dialysis facilities in Japan. Over 70% of haemodialysis incidents were reported as problems or complications related to dialyser, circuit, medication and setting of dialysis condition. Approximately 70% of errors took place immediately before and after the four hours of haemodialysis therapy. Error types most frequently made in the dialysis unit were omission and qualitative errors. Failures or complications classified to staff human factors, communication, task and organisational factors were found in most dialysis incidents. Device/equipment/materials, medicine and clinical documents were most likely to be involved in errors. Haemodialysis nurses were involved in more incidents related to medicine and documents, whereas dialysis technologists made more errors with device/equipment/materials. This error taxonomy system is able to investigate incidents and adverse events occurring in the dialysis setting but is also able to estimate safety-related status of an organisation, such as reporting culture. © 2014 European Dialysis and Transplant Nurses Association/European Renal Care Association.

Correcting evaluation bias of relational classifiers with network cross validation

DOE PAGES

Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; ...

2011-01-04

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess themore » models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).« less

Comparison of disease prevalence in two populations in the presence of misclassification.

PubMed

Tang, Man-Lai; Qiu, Shi-Fang; Poon, Wai-Yin

2012-11-01

Comparing disease prevalence in two groups is an important topic in medical research, and prevalence rates are obtained by classifying subjects according to whether they have the disease. Both high-cost infallible gold-standard classifiers or low-cost fallible classifiers can be used to classify subjects. However, statistical analysis that is based on data sets with misclassifications leads to biased results. As a compromise between the two classification approaches, partially validated sets are often used in which all individuals are classified by fallible classifiers, and some of the individuals are validated by the accurate gold-standard classifiers. In this article, we develop several reliable test procedures and approximate sample size formulas for disease prevalence studies based on the difference between two disease prevalence rates with two independent partially validated series. Empirical studies show that (i) the Score test produces close-to-nominal level and is preferred in practice; and (ii) the sample size formula based on the Score test is also fairly accurate in terms of the empirical power and type I error rate, and is hence recommended. A real example from an aplastic anemia study is used to illustrate the proposed methodologies. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Medication errors reported to the National Medication Error Reporting System in Malaysia: a 4-year retrospective review (2009 to 2012).

PubMed

Samsiah, A; Othman, Noordin; Jamshed, Shazia; Hassali, Mohamed Azmi; Wan-Mohaina, W M

2016-12-01

Reporting and analysing the data on medication errors (MEs) is important and contributes to a better understanding of the error-prone environment. This study aims to examine the characteristics of errors submitted to the National Medication Error Reporting System (MERS) in Malaysia. A retrospective review of reports received from 1 January 2009 to 31 December 2012 was undertaken. Descriptive statistics method was applied. A total of 17,357 MEs reported were reviewed. The majority of errors were from public-funded hospitals. Near misses were classified in 86.3 % of the errors. The majority of errors (98.1 %) had no harmful effects on the patients. Prescribing contributed to more than three-quarters of the overall errors (76.1 %). Pharmacists detected and reported the majority of errors (92.1 %). Cases of erroneous dosage or strength of medicine (30.75 %) were the leading type of error, whilst cardiovascular (25.4 %) was the most common category of drug found. MERS provides rich information on the characteristics of reported MEs. Low contribution to reporting from healthcare facilities other than government hospitals and non-pharmacists requires further investigation. Thus, a feasible approach to promote MERS among healthcare providers in both public and private sectors needs to be formulated and strengthened. Preventive measures to minimise MEs should be directed to improve prescribing competency among the fallible prescribers identified.

Error Model and Compensation of Bell-Shaped Vibratory Gyro

PubMed Central

Su, Zhong; Liu, Ning; Li, Qing

2015-01-01

A bell-shaped vibratory angular velocity gyro (BVG), inspired by the Chinese traditional bell, is a type of axisymmetric shell resonator gyroscope. This paper focuses on development of an error model and compensation of the BVG. A dynamic equation is firstly established, based on a study of the BVG working mechanism. This equation is then used to evaluate the relationship between the angular rate output signal and bell-shaped resonator character, analyze the influence of the main error sources and set up an error model for the BVG. The error sources are classified from the error propagation characteristics, and the compensation method is presented based on the error model. Finally, using the error model and compensation method, the BVG is calibrated experimentally including rough compensation, temperature and bias compensation, scale factor compensation and noise filter. The experimentally obtained bias instability is from 20.5°/h to 4.7°/h, the random walk is from 2.8°/h1/2 to 0.7°/h1/2 and the nonlinearity is from 0.2% to 0.03%. Based on the error compensation, it is shown that there is a good linear relationship between the sensing signal and the angular velocity, suggesting that the BVG is a good candidate for the field of low and medium rotational speed measurement. PMID:26393593

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

PubMed

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

PubMed

Zheng, Wenjing; Balzer, Laura; van der Laan, Mark; Petersen, Maya

2018-01-30

Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency virus prevention program based on offering pre-exposure prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program. In this article, we consider a general class of constrained binary classification problems wherein the objective function and the constraint are both monotonic with respect to a threshold. These include the minimization of the rate of positive predictions subject to a minimum sensitivity, the maximization of sensitivity subject to a maximum rate of positive predictions, and the Neyman-Pearson paradigm, which minimizes the type II error subject to an upper bound on the type I error. We propose an ensemble approach to these binary classification problems based on the Super Learner methodology. This approach linearly combines a user-supplied library of scoring algorithms, with combination weights and a discriminating threshold chosen to minimize the constrained optimality criterion. We then illustrate the application of the proposed classifier to develop an individualized PrEP targeting strategy in a resource-limited setting, with the goal of minimizing the number of PrEP offerings while achieving a minimum required sensitivity. This proof of concept data analysis uses baseline data from the ongoing Sustainable East Africa Research in Community Health study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

High performance interconnection between high data rate networks

NASA Technical Reports Server (NTRS)

Foudriat, E. C.; Maly, K.; Overstreet, C. M.; Zhang, L.; Sun, W.

1992-01-01

The bridge/gateway system needed to interconnect a wide range of computer networks to support a wide range of user quality-of-service requirements is discussed. The bridge/gateway must handle a wide range of message types including synchronous and asynchronous traffic, large, bursty messages, short, self-contained messages, time critical messages, etc. It is shown that messages can be classified into three basic classes, synchronous and large and small asynchronous messages. The first two require call setup so that packet identification, buffer handling, etc. can be supported in the bridge/gateway. Identification enables resequences in packet size. The third class is for messages which do not require call setup. Resequencing hardware based to handle two types of resequencing problems is presented. The first is for a virtual parallel circuit which can scramble channel bytes. The second system is effective in handling both synchronous and asynchronous traffic between networks with highly differing packet sizes and data rates. The two other major needs for the bridge/gateway are congestion and error control. A dynamic, lossless congestion control scheme which can easily support effective error correction is presented. Results indicate that the congestion control scheme provides close to optimal capacity under congested conditions. Under conditions where error may develop due to intervening networks which are not lossless, intermediate error recovery and correction takes 1/3 less time than equivalent end-to-end error correction under similar conditions.

Errors in accident data, its types, causes and methods of rectification-analysis of the literature.

PubMed

Ahmed, Ashar; Sadullah, Ahmad Farhan Mohd; Yahya, Ahmad Shukri

2017-07-29

Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Methods for data classification

DOEpatents

Garrity, George [Okemos, MI; Lilburn, Timothy G [Front Royal, VA

2011-10-11

The present invention provides methods for classifying data and uncovering and correcting annotation errors. In particular, the present invention provides a self-organizing, self-correcting algorithm for use in classifying data. Additionally, the present invention provides a method for classifying biological taxa.

Satellite inventory of Minnesota forest resources

NASA Technical Reports Server (NTRS)

Bauer, Marvin E.; Burk, Thomas E.; Ek, Alan R.; Coppin, Pol R.; Lime, Stephen D.; Walsh, Terese A.; Walters, David K.; Befort, William; Heinzen, David F.

1993-01-01

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Error analysis of mathematics students who are taught by using the book of mathematics learning strategy in solving pedagogical problems based on Polya’s four-step approach

NASA Astrophysics Data System (ADS)

Halomoan Siregar, Budi; Dewi, Izwita; Andriani, Ade

2018-03-01

The purpose of this study is to analyse the types of students errors and causes of them in solving of pedagogic problems. The type of this research is qualitative descriptive, conducted on 34 students of mathematics education in academic year 2017 to 2018. The data in this study is obtained through interviews and tests. Furthermore, the data is then analyzed through three stages: 1) data reduction, 2) data description, and 3) conclusions. The data is reduced by organizing and classifying them in order to obtain meaningful information. After reducing, then the data presented in a simple form of narrative, graphics, and tables to illustrate clearly the errors of students. Based on the information then drawn a conclusion. The results of this study indicate that the students made various errors: 1) they made a mistake in answer what being asked at the problem, because they misunderstood the problem, 2) they fail to plan the learning process based on constructivism, due to lack of understanding of how to design the learning, 3) they determine an inappropriate learning tool, because they did not understand what kind of learning tool is relevant to use.

The Impact of Incident Disclosure Behaviors on Medical Malpractice Claims.

PubMed

Giraldo, Priscila; Sato, Luke; Castells, Xavier

2017-06-30

To provide preliminary estimates of incident disclosure behaviors on medical malpractice claims. We conducted a descriptive analysis of data on medical malpractice claims obtained from the Controlled Risk Insurance Company and Risk Management Foundation of Harvard Medical Institutions (Cambridge, Massachusetts) between 2012 and 2013 (n = 434). The characteristics of disclosure and apology after medical errors were analyzed. Of 434 medical malpractice claims, 4.6% (n = 20) medical errors had been disclosed to the patient at the time of the error, and 5.9% (n = 26) had been followed by disclosure and apology. The highest number of disclosed injuries occurred in 2011 (23.9%; n = 11) and 2012 (34.8%; n = 16). There was no incremental increase during the financial years studied (2012-2013). The mean age of informed patients was 52.96 years, 58.7 % of the patients were female, and 52.2% were inpatients. Of the disclosed errors, 26.1% led to an adverse reaction, and 17.4% were fatal. The cause of disclosed medical error was improper surgical performance in 17.4% (95% confidence interval, 6.4-28.4). Disclosed medical errors were classified as medium severity in 67.4%. No apology statement was issued in 54.5% of medical errors classified as high severity. At the health-care centers studied, when a claim followed a medical error, providers infrequently disclosed medical errors or apologized to the patient or relatives. Most of the medical errors followed by disclosure and apology were classified as being of high and medium severity. No changes were detected in the volume of lawsuits over time.

Detecting paroxysmal coughing from pertussis cases using voice recognition technology.

PubMed

Parker, Danny; Picone, Joseph; Harati, Amir; Lu, Shuang; Jenkyns, Marion H; Polgreen, Philip M

2013-01-01

Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.

Speech Errors across the Lifespan

ERIC Educational Resources Information Center

Vousden, Janet I.; Maylor, Elizabeth A.

2006-01-01

Dell, Burger, and Svec (1997) proposed that the proportion of speech errors classified as anticipations (e.g., "moot and mouth") can be predicted solely from the overall error rate, such that the greater the error rate, the lower the anticipatory proportion (AP) of errors. We report a study examining whether this effect applies to changes in error…

AdaBoost-based algorithm for network intrusion detection.

PubMed

Hu, Weiming; Hu, Wei; Maybank, Steve

2008-04-01

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

Effect of lethality on the extinction and on the error threshold of quasispecies.

PubMed

Tejero, Hector; Marín, Arturo; Montero, Francisco

2010-02-21

In this paper the effect of lethality on error threshold and extinction has been studied in a population of error-prone self-replicating molecules. For given lethality and a simple fitness landscape, three dynamic regimes can be obtained: quasispecies, error catastrophe, and extinction. Using a simple model in which molecules are classified as master, lethal and non-lethal mutants, it is possible to obtain the mutation rates of the transitions between the three regimes analytically. The numerical resolution of the extended model, in which molecules are classified depending on their Hamming distance to the master sequence, confirms the results obtained in the simple model and shows how an error catastrophe regime changes when lethality is taken in account. (c) 2009 Elsevier Ltd. All rights reserved.

Error compensation of single-antenna attitude determination using GNSS for Low-dynamic applications

NASA Astrophysics Data System (ADS)

Chen, Wen; Yu, Chao; Cai, Miaomiao

2017-04-01

GNSS-based single-antenna pseudo-attitude determination method has attracted more and more attention from the field of high-dynamic navigation due to its low cost, low system complexity, and no temporal accumulated errors. Related researches indicate that this method can be an important complement or even an alternative to the traditional sensors for general accuracy requirement (such as small UAV navigation). The application of single-antenna attitude determining method to low-dynamic carrier has just started. Different from the traditional multi-antenna attitude measurement technique, the pseudo-attitude attitude determination method calculates the rotation angle of the carrier trajectory relative to the earth. Thus it inevitably contains some deviations comparing with the real attitude angle. In low-dynamic application, these deviations are particularly noticeable, which may not be ignored. The causes of the deviations can be roughly classified into three categories, including the measurement error, the offset error, and the lateral error. Empirical correction strategies for the formal two errors have been promoted in previous study, but lack of theoretical support. In this paper, we will provide quantitative description of the three type of errors and discuss the related error compensation methods. Vehicle and shipborne experiments were carried out to verify the feasibility of the proposed correction methods. Keywords: Error compensation; Single-antenna; GNSS; Attitude determination; Low-dynamic

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Evidence-based anatomical review areas derived from systematic analysis of cases from a radiological departmental discrepancy meeting.

PubMed

Chin, S C; Weir-McCall, J R; Yeap, P M; White, R D; Budak, M J; Duncan, G; Oliver, T B; Zealley, I A

2017-10-01

To produce short checklists of specific anatomical review sites for different regions of the body based on the frequency of radiological errors reviewed at radiology discrepancy meetings, thereby creating "evidence-based" review areas for radiology reporting. A single centre discrepancy database was retrospectively reviewed from a 5-year period. All errors were classified by type, modality, body system, and specific anatomical location. Errors were assigned to one of four body regions: chest, abdominopelvic, central nervous system (CNS), and musculoskeletal (MSK). Frequencies of errors in anatomical locations were then analysed. There were 561 errors in 477 examinations; 290 (46%) errors occurred in the abdomen/pelvis, 99 (15.7%) in the chest, 117 (18.5%) in the CNS, and 125 (19.9%) in the MSK system. In each body system, the five most common location were chest: lung bases on computed tomography (CT), apices on radiography, pulmonary vasculature, bones, and mediastinum; abdominopelvic: vasculature, colon, kidneys, liver, and pancreas; CNS: intracranial vasculature, peripheral cerebral grey matter, bone, parafalcine, and the frontotemporal lobes surrounding the Sylvian fissure; and MSK: calvarium, sacrum, pelvis, chest, and spine. The five listed locations accounted for >50% of all perceptual errors suggesting an avenue for focused review at the end of reporting. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Classification of echolocation clicks from odontocetes in the Southern California Bight.

PubMed

Roch, Marie A; Klinck, Holger; Baumann-Pickering, Simone; Mellinger, David K; Qui, Simon; Soldevilla, Melissa S; Hildebrand, John A

2011-01-01

This study presents a system for classifying echolocation clicks of six species of odontocetes in the Southern California Bight: Visually confirmed bottlenose dolphins, short- and long-beaked common dolphins, Pacific white-sided dolphins, Risso's dolphins, and presumed Cuvier's beaked whales. Echolocation clicks are represented by cepstral feature vectors that are classified by Gaussian mixture models. A randomized cross-validation experiment is designed to provide conditions similar to those found in a field-deployed system. To prevent matched conditions from inappropriately lowering the error rate, echolocation clicks associated with a single sighting are never split across the training and test data. Sightings are randomly permuted before assignment to folds in the experiment. This allows different combinations of the training and test data to be used while keeping data from each sighting entirely in the training or test set. The system achieves a mean error rate of 22% across 100 randomized three-fold cross-validation experiments. Four of the six species had mean error rates lower than the overall mean, with the presumed Cuvier's beaked whale clicks showing the best performance (<2% error rate). Long-beaked common and bottlenose dolphins proved the most difficult to classify, with mean error rates of 53% and 68%, respectively.

Pattern recognition of native plant communities: Manitou Colorado test site

NASA Technical Reports Server (NTRS)

Driscoll, R. S.

1972-01-01

Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.

The Surveillance Error Grid

PubMed Central

Lias, Courtney; Vigersky, Robert; Clarke, William; Parkes, Joan Lee; Sacks, David B.; Kirkman, M. Sue; Kovatchev, Boris

2014-01-01

Introduction: Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. Methods: A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. Results: SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. Discussion: The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs. PMID:25562886

The surveillance error grid.

PubMed

Klonoff, David C; Lias, Courtney; Vigersky, Robert; Clarke, William; Parkes, Joan Lee; Sacks, David B; Kirkman, M Sue; Kovatchev, Boris

2014-07-01

Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs. © 2014 Diabetes Technology Society.

Risk behaviours for organism transmission in health care delivery-A two month unstructured observational study.

PubMed

Lindberg, Maria; Lindberg, Magnus; Skytt, Bernice

2017-05-01

Errors in infection control practices risk patient safety. The probability for errors can increase when care practices become more multifaceted. It is therefore fundamental to track risk behaviours and potential errors in various care situations. The aim of this study was to describe care situations involving risk behaviours for organism transmission that could lead to subsequent healthcare-associated infections. Unstructured nonparticipant observations were performed at three medical wards. Healthcare personnel (n=27) were shadowed, in total 39h, on randomly selected weekdays between 7:30 am and 12 noon. Content analysis was used to inductively categorize activities into tasks and based on the character into groups. Risk behaviours for organism transmission were deductively classified into types of errors. Multiple response crosstabs procedure was used to visualize the number and proportion of errors in tasks. One-Way ANOVA with Bonferroni post Hoc test was used to determine differences among the three groups of activities. The qualitative findings gives an understanding of that risk behaviours for organism transmission goes beyond the five moments of hand hygiene and also includes the handling and placement of materials and equipment. The tasks with the highest percentage of errors were; 'personal hygiene', 'elimination' and 'dressing/wound care'. The most common types of errors in all identified tasks were; 'hand disinfection', 'glove usage', and 'placement of materials'. Significantly more errors (p<0.0001) were observed the more multifaceted (single, combined or interrupted) the activity was. The numbers and types of errors as well as the character of activities performed in care situations described in this study confirm the need to improve current infection control practices. It is fundamental that healthcare personnel practice good hand hygiene however effective preventive hygiene is complex in healthcare activities due to the multifaceted care situations, especially when activities are interrupted. A deeper understanding of infection control practices that goes beyond the sense of security by means of hand disinfection and use of gloves is needed as materials and surfaces in the care environment might be contaminated and thus pose a risk for organism transmission. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

PubMed

Freedson, Patty S; Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

2011-12-01

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Infovigilance: reporting errors in official drug information sources.

PubMed

Fusier, Isabelle; Tollier, Corinne; Husson, Marie-Caroline

2005-06-01

The French drug database Thériaque (http://www.theriaque.org) developed by the (Centre National Hospitalier d'Information sur le Médicament) (CNHIM), is responsible for the dissemination of independent information about all drugs available in France. Each month the CNHIM pharmacists report problems due to inaccuracies in these sources to the French drug agency. In daily practice we devised the term "infovigilance": "Activity of error or inaccuracy notification in information sources which could be responsible for medication errors". The aim of this study was to evaluate the impact of CNHIM infovigilance on the contents of the Summary of Product Characteristics (SPCs). The study was a prospective study from 09/11/2001 to 31/12/2002. The problems related to the quality of information were classified into four types (inaccuracy/confusion, error/lack of information, discordance between SPC sections and discordance between generic SPCs). (1) Number of notifications and number of SPCs integrated into the database during the study period. (2) Percentage of notifications for each type: with or without potential patient impact, with or without later correction of the SPC, per section. 2.7% (85/3151) of SPCs integrated into the database were concerned by a notification of a problem. Notifications according to type of problem were inaccuracy/confusion (32%), error/lack of information (13%), discordance between SPC sections (27%) and discordance between generic SPCs (28%). 55% of problems were evaluated as 'likely to have an impact on the patient' and 45% as 'unlikely to have an impact on the patient'. 22 of problems which have been reported to the French drug agency were corrected and new updated SPCs were published with the corrections. Our efforts to improve the quality of drug information sources through a continuous "infovigilance" process need to be continued and extended to other information sources.

Calculation and evaluation of sediment effect concentrations for the amphipod Hyalella azteca and the midge Chironomus riparius

USGS Publications Warehouse

Ingersoll, Christopher G.; Haverland, Pamela S.; Brunson, Eric L.; Canfield, Timothy J.; Dwyer, F. James; Henke, Chris; Kemble, Nile E.; Mount, David R.; Fox, Richard G.

1996-01-01

Procedures are described for calculating and evaluating sediment effect concentrations (SECs) using laboratory data on the toxicity of contaminants associated with field-collected sediment to the amphipod Hyalella azteca and the midge Chironomus riparius. SECs are defined as the concentrations of individual contaminants in sediment below which toxicity is rarely observed and above which toxicity is frequently observed. The objective of the present study was to develop SECs to classify toxicity data for Great Lake sediment samples tested with Hyalella azteca and Chironomus riparius. This SEC database included samples from additional sites across the United States in order to make the database as robust as possible. Three types of SECs were calculated from these data: (1) Effect Range Low (ERL) and Effect Range Median (ERM), (2) Threshold Effect Level (TEL) and Probable Effect Level (PEL), and (3) No Effect Concentration (NEC). We were able to calculate SECs primarily for total metals, simultaneously extracted metals, polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The ranges of concentrations in sediment were too narrow in our database to adequately evaluate SECs for butyltins, methyl mercury, polychlorinated dioxins and furans, or chlorinated pesticides. About 60 to 80% of the sediment samples in the database are correctly classified as toxic or not toxic depending on type of SEC evaluated. ERMs and ERLs are generally as reliable as paired PELs and TELs at classifying both toxic and non-toxic samples in our database. Reliability of the SECs in terms of correctly classifying sediment samples is similar between ERMs and NECs; however, ERMs minimize Type I error (false positives) relative to ERLs and minimize Type II error (false negatives) relative to NECs. Correct classification of samples can be improved by using only the most reliable individual SECs for chemicals (i.e., those with a higher percentage of correct classification). SECs calculated using sediment concentrations normalized to total organic carbon (TOC) concentrations did not improve the reliability compared to SECs calculated using dry-weight concentrations. The range of TOC concentrations in our database was relatively narrow compared to the ranges of contaminant concentrations. Therefore, normalizing dry-weight concentrations to a relatively narrow range of TOC concentrations had little influence on relative concentra of contaminants among samples. When SECs are used to conduct a preliminary screening to predict the potential for toxicity in the absence of actual toxicity testing, a low number of SEC exceedances should be used to minimize the potential for false negatives; however, the risk of accepting higher false positives is increased.

Automated spectral classification and the GAIA project

NASA Technical Reports Server (NTRS)

Lasala, Jerry; Kurtz, Michael J.

1995-01-01

Two dimensional spectral types for each of the stars observed in the global astrometric interferometer for astrophysics (GAIA) mission would provide additional information for the galactic structure and stellar evolution studies, as well as helping in the identification of unusual objects and populations. The classification of the large quantity generated spectra requires that automated techniques are implemented. Approaches for the automatic classification are reviewed, and a metric-distance method is discussed. In tests, the metric-distance method produced spectral types with mean errors comparable to those of human classifiers working at similar resolution. Data and equipment requirements for an automated classification survey, are discussed. A program of auxiliary observations is proposed to yield spectral types and radial velocities for the GAIA-observed stars.

Combating speckle in SAR images - Vector filtering and sequential classification based on a multiplicative noise model

NASA Technical Reports Server (NTRS)

Lin, Qian; Allebach, Jan P.

1990-01-01

An adaptive vector linear minimum mean-squared error (LMMSE) filter for multichannel images with multiplicative noise is presented. It is shown theoretically that the mean-squared error in the filter output is reduced by making use of the correlation between image bands. The vector and conventional scalar LMMSE filters are applied to a three-band SIR-B SAR, and their performance is compared. Based on a mutliplicative noise model, the per-pel maximum likelihood classifier was derived. The authors extend this to the design of sequential and robust classifiers. These classifiers are also applied to the three-band SIR-B SAR image.

Authentication of the botanical and geographical origin of honey by mid-infrared spectroscopy.

PubMed

Ruoff, Kaspar; Luginbühl, Werner; Künzli, Raphael; Iglesias, María Teresa; Bogdanov, Stefan; Bosset, Jacques Olivier; von der Ohe, Katharina; von der Ohe, Werner; Amado, Renato

2006-09-06

The potential of Fourier transform mid-infrared spectroscopy (FT-MIR) using an attenuated total reflectance (ATR) cell was evaluated for the authentication of 11 unifloral (acacia, alpine rose, chestnut, dandelion, heather, lime, rape, fir honeydew, metcalfa honeydew, oak honeydew) and polyfloral honey types (n = 411 samples) previously classified with traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis, the error rates of the discriminant models being calculated by using Bayes' theorem. The error rates ranged from <0.1% (polyfloral and heather honeys as well as honeydew honeys from metcalfa, oak, and fir) to 8.3% (alpine rose honey) in both jackknife classification and validation, depending on the honey type considered. This study indicates that ATR-MIR spectroscopy is a valuable tool for the authentication of the botanical origin and quality control and may also be useful for the determination of the geographical origin of honey.

Spin Contamination Error in Optimized Geometry of Singlet Carbene (1A1) by Broken-Symmetry Method

NASA Astrophysics Data System (ADS)

Kitagawa, Yasutaka; Saito, Toru; Nakanishi, Yasuyuki; Kataoka, Yusuke; Matsui, Toru; Kawakami, Takashi; Okumura, Mitsutaka; Yamaguchi, Kizashi

2009-10-01

Spin contamination errors of a broken-symmetry (BS) method in optimized structural parameters of the singlet methylene (1A1) molecule are quantitatively estimated for the Hartree-Fock (HF) method, post-HF methods (CID, CCD, MP2, MP3, MP4(SDQ)), and a hybrid DFT (B3LYP) method. For the purpose, the optimized geometry by the BS method is compared with that of an approximate spin projection (AP) method. The difference between the BS and the AP methods is about 10-20° in the HCH angle. In order to examine the basis set dependency of the spin contamination error, calculated results by STO-3G, 6-31G*, and 6-311++G** are compared. The error depends on the basis sets, but the tendencies of each method are classified into two types. Calculated energy splitting values between the triplet and the singlet states (ST gap) indicate that the contamination of the stable triplet state makes the BS singlet solution stable and the ST gap becomes small. The energy order of the spin contamination error in the ST gap is estimated to be 10-1 eV.

Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

ERIC Educational Resources Information Center

Ye, Qiang

2010-01-01

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

Modeling Types of Pedal Applications Using a Driving Simulator.

PubMed

Wu, Yuqing; Boyle, Linda Ng; McGehee, Daniel; Roe, Cheryl A; Ebe, Kazutoshi; Foley, James

2015-11-01

The aim of this study was to examine variations in drivers' foot behavior and identify factors associated with pedal misapplications. Few studies have focused on the foot behavior while in the vehicle and the mishaps that a driver can encounter during a potentially hazardous situation. A driving simulation study was used to understand how drivers move their right foot toward the pedals. The study included data from 43 drivers as they responded to a series of rapid traffic signal phase changes. Pedal application types were classified as (a) direct hit, (b) hesitated, (c) corrected trajectory, and (d) pedal errors (incorrect trajectories, misses, slips, or pressed both pedals). A mixed-effects multinomial logit model was used to predict the likelihood of one of these pedal applications, and linear mixed models with repeated measures were used to examine the response time and pedal duration given the various experimental conditions (stimuli color and location). Younger drivers had higher probabilities of direct hits when compared to other age groups. Participants tended to have more pedal errors when responding to a red signal or when the signal appeared to be closer. Traffic signal phases and locations were associated with pedal response time and duration. The response time and pedal duration affected the likelihood of being in one of the four pedal application types. Findings from this study suggest that age-related and situational factors may play a role in pedal errors, and the stimuli locations could affect the type of pedal application. © 2015, Human Factors and Ergonomics Society.

VizieR Online Data Catalog: Spitzer Atlas of Stellar Spectra (SASS) (Ardila+, 2010)

NASA Astrophysics Data System (ADS)

Ardila, D. R.; van Dyk, S. D.; Makowiecki, W.; Stauffer, J.; Song, I.; Rho, J.; Fajardo-Acosta, S.; Hoard, D. W.; Wachter, S.

2010-11-01

From IRS Staring observations in the Spitzer archive we selected those stellar targets that had been observed with all the low-resolution IRS modules. We did not include known young stars with circumstellar material, stars known to harbor debris disks, or objects classified in SIMBAD as RS CVn, Be stars, or eclipsing binaries. We have also avoided classes already fully described with IRAS, ISO, or Spitzer, such as Asymptotic Giant Branch stars and rejected targets presenting IR excesses. However, note that in the case of very massive and/or evolved stars there are few objects presenting a pure photospheric spectrum. A few stars are specifically selected for their intrinsic interest regardless of their IR excess and even if the Atlas already contained another star with the same spectral type. The spectral coverage only reaches to 14um in the case of very late spectral classes (late M, L and T dwarfs) and some WR stars for which the long wavelength modules are unusable or not present in the archive. The spectral types have been taken from (in order of priority): * NStED (http://nsted.ipac.caltech.edu/), * NStars (http://nstars.nau.edu/nau_nstars/about.htm), * the Tycho-2 Spectral Type Catalog (Cat. III/231) * SIMBAD. For certain types of objects, we have used specialized catalogs as the source of the spectral types. The data were processed with the Spitzer Science Center S18.7.0 pipelined and corrected for teardrop effects, slit position uncertainties, residual flat-field errors, residual model errors, 24um flux deficit (1), fringing, and order mismatches. The Atlas files contain an error value for each wavelength, intended to represent the random 1sig error at that wavelength. This is the error provided by the SSC's S18.7.0 pipeline and propagated along the reduction procedure. The treatment of errors remains incomplete in this pipeline (2). The errors provided here should be considered carefully, before propagating them into further calculations. However, the processing insures that the spectra do not have strong spurious emission or absorption lines in large signal-to-noise regions. (1) http://ssc.spitzer.caltech.edu/irs/irsinstrumenthandbook/102/ #Toc253561116 (2) http://ssc.spitzer.caltech.edu/irs/irsinstrumenthandbook/ (4 data files).

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging.

PubMed

Iannaccone, Reto; Hauser, Tobias U; Ball, Juliane; Brandeis, Daniel; Walitza, Susanne; Brem, Silvia

2015-10-01

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

Unreliable patient identification warrants ABO typing at admission to check existing records before transfusion.

PubMed

Ferrera-Tourenc, V; Lassale, B; Chiaroni, J; Dettori, I

2015-06-01

This study describes patient identification errors leading to transfusional near-misses in blood issued by the Alps Mediterranean French Blood Establishment (EFSAM) to Marseille Public Hospitals (APHM) over an 18-month period. The EFSAM consolidates 14 blood banks in southeast France. It supplies 149 hospitals and maintains a centralized database on ABO types used at all area hospitals. As an added precaution against incompatible transfusion, the APHM requires ABO testing at each admission regardless of whether the patient has an ABO record. The study goal was to determine if admission testing was warranted. Discrepancies between ABO type determined by admission testing and records in the centralized database were investigated. The root cause for each discrepancy was classified as specimen collection or patient admission error. Causes of patient admission events were further subclassified as namesake (name similarity) or impersonation (identity fraud). The incidence of ABO discrepancies was 1:2334 including a 1:3329 incidence of patient admission events. Impersonation was the main cause of identity events accounting for 90.3% of cases. The APHM's ABO control policy prevented 19 incompatible transfusions. In relation to the 48,593 packed red cell units transfused, this would have corresponded to a risk of 1:2526. Collecting and storing ABO typing results in a centralized database is an essential public health tool. It allows crosschecking of current test results with past records and avoids redundant testing. However, as patient identification remains unreliable, ABO typing at each admission is still warranted to prevent transfusion errors. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Difference in Perseverative Errors during a Visual Attention Task with Auditory Distractors in Alpha-9 Nicotinic Receptor Subunit Wild Type and Knock-Out Mice.

PubMed

Jorratt, Pascal; Delano, Paul H; Delgado, Carolina; Dagnino-Subiabre, Alexies; Terreros, Gonzalo

2017-01-01

The auditory efferent system is a neural network that originates in the auditory cortex and projects to the cochlear receptor through olivocochlear (OC) neurons. Medial OC neurons make cholinergic synapses with outer hair cells (OHCs) through nicotinic receptors constituted by α9 and α10 subunits. One of the physiological functions of the α9 nicotinic receptor subunit (α9-nAChR) is the suppression of auditory distractors during selective attention to visual stimuli. In a recent study we demonstrated that the behavioral performance of alpha-9 nicotinic receptor knock-out (KO) mice is altered during selective attention to visual stimuli with auditory distractors since they made less correct responses and more omissions than wild type (WT) mice. As the inhibition of the behavioral responses to irrelevant stimuli is an important mechanism of the selective attention processes, behavioral errors are relevant measures that can reflect altered inhibitory control. Errors produced during a cued attention task can be classified as premature, target and perseverative errors. Perseverative responses can be considered as an inability to inhibit the repetition of an action already planned, while premature responses can be considered as an index of the ability to wait or retain an action. Here, we studied premature, target and perseverative errors during a visual attention task with auditory distractors in WT and KO mice. We found that α9-KO mice make fewer perseverative errors with longer latencies than WT mice in the presence of auditory distractors. In addition, although we found no significant difference in the number of target error between genotypes, KO mice made more short-latency target errors than WT mice during the presentation of auditory distractors. The fewer perseverative error made by α9-KO mice could be explained by a reduced motivation for reward and an increased impulsivity during decision making with auditory distraction in KO mice.

Error Analysis in Composition of Iranian Lower Intermediate Students

ERIC Educational Resources Information Center

Taghavi, Mehdi

2012-01-01

Learners make errors during the process of learning languages. This study examines errors in writing task of twenty Iranian lower intermediate male students aged between 13 and 15. A subject was given to the participants was a composition about the seasons of a year. All of the errors were identified and classified. Corder's classification (1967)…

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

Yukinawa, N.; Takenouchi, T.; Oba, S.; Ishii, S.

2008-01-01

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.

The pot calling the kettle black: the extent and type of errors in a computerized immunization registry and by parent report.

PubMed

MacDonald, Shannon E; Schopflocher, Donald P; Golonka, Richard P

2014-01-04

Accurate classification of children's immunization status is essential for clinical care, administration and evaluation of immunization programs, and vaccine program research. Computerized immunization registries have been proposed as a valuable alternative to provider paper records or parent report, but there is a need to better understand the challenges associated with their use. This study assessed the accuracy of immunization status classification in an immunization registry as compared to parent report and determined the number and type of errors occurring in both sources. This study was a sub-analysis of a larger study which compared the characteristics of children whose immunizations were up to date (UTD) at two years as compared to those not UTD. Children's immunization status was initially determined from a population-based immunization registry, and then compared to parent report of immunization status, as reported in a postal survey. Discrepancies between the two sources were adjudicated by review of immunization providers' hard-copy clinic records. Descriptive analyses included calculating proportions and confidence intervals for errors in classification and reporting of the type and frequency of errors. Among the 461 survey respondents, there were 60 discrepancies in immunization status. The majority of errors were due to parent report (n = 44), but the registry was not without fault (n = 16). Parents tended to erroneously report their child as UTD, whereas the registry was more likely to wrongly classify children as not UTD. Reasons for registry errors included failure to account for varicella disease history, variable number of doses required due to age at series initiation, and doses administered out of the region. These results confirm that parent report is often flawed, but also identify that registries are prone to misclassification of immunization status. Immunization program administrators and researchers need to institute measures to identify and reduce misclassification, in order for registries to play an effective role in the control of vaccine-preventable disease.

The pot calling the kettle black: the extent and type of errors in a computerized immunization registry and by parent report

PubMed Central

2014-01-01

Background Accurate classification of children’s immunization status is essential for clinical care, administration and evaluation of immunization programs, and vaccine program research. Computerized immunization registries have been proposed as a valuable alternative to provider paper records or parent report, but there is a need to better understand the challenges associated with their use. This study assessed the accuracy of immunization status classification in an immunization registry as compared to parent report and determined the number and type of errors occurring in both sources. Methods This study was a sub-analysis of a larger study which compared the characteristics of children whose immunizations were up to date (UTD) at two years as compared to those not UTD. Children’s immunization status was initially determined from a population-based immunization registry, and then compared to parent report of immunization status, as reported in a postal survey. Discrepancies between the two sources were adjudicated by review of immunization providers’ hard-copy clinic records. Descriptive analyses included calculating proportions and confidence intervals for errors in classification and reporting of the type and frequency of errors. Results Among the 461 survey respondents, there were 60 discrepancies in immunization status. The majority of errors were due to parent report (n = 44), but the registry was not without fault (n = 16). Parents tended to erroneously report their child as UTD, whereas the registry was more likely to wrongly classify children as not UTD. Reasons for registry errors included failure to account for varicella disease history, variable number of doses required due to age at series initiation, and doses administered out of the region. Conclusions These results confirm that parent report is often flawed, but also identify that registries are prone to misclassification of immunization status. Immunization program administrators and researchers need to institute measures to identify and reduce misclassification, in order for registries to play an effective role in the control of vaccine-preventable disease. PMID:24387002

Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue.

PubMed

Wei, Zhenbo; Wang, Jun; Ye, Linshuang

2011-08-15

A voltammetric electronic tongue (VE-tongue) was developed to discriminate the difference between Chinese rice wines in this research. Three types of Chinese rice wine with different marked ages (1, 3, and 5 years) were classified by the VE-tongue by principal component analysis (PCA) and cluster analysis (CA). The VE-tongue consisted of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The multi-frequency large amplitude pulse voltammetry (MLAPV), which consisted of four segments of 1 Hz, 10 Hz, 100 Hz, and 1000 Hz, was applied as the potential waveform. The three types of Chinese rice wine could be classified accurately by PCA and CA, and some interesting regularity is shown in the score plots with the help of PCA. Two regression models, partial least squares (PLS) and back-error propagation-artificial neural network (BP-ANN), were used for wine age prediction. The regression results showed that the marked ages of the three types of Chinese rice wine were successfully predicted using PLS and BP-ANN. Copyright © 2011 Elsevier B.V. All rights reserved.

Evaluation of the NASA GISS AR5 SCM/GCM at the ARM SGP Site using Self Organizing Maps

NASA Astrophysics Data System (ADS)

Kennedy, A. D.; Dong, X.; Xi, B.; Del Genio, A. D.; Wolf, A.

2011-12-01

Understanding and improving clouds in climate models requires moving beyond comparing annual and seasonal means. Errors can offset resulting in models getting the right long-term solution for the wrong reasons. For example, cloud parameterization errors may be balanced by the model incorrectly simulating the frequency distribution of atmospheric states. To faithfully evaluate climate models it is necessary to partition results into specific regimes. This has been completed in the past by evaluating models by their ability to produce cloud regimes as determined by observational products from satellites. An alternative approach is to first classify meteorological regimes (i.e., synoptic pattern and forcing) and then determine what types of clouds occur for each class. In this study, a competitive neural network known as the Self Organizing Map (SOM) is first used to classify synoptic patterns from a reanalysis over the Southern Great Plains (SGP) region during the period 1999-2008. These results are then used to evaluate simulated clouds from the AR5 version of the NASA GISS Model E Single Column Model (SCM). Unlike past studies that narrowed classes into several categories, this study assumes that the atmosphere is capable of producing an infinite amount of states. As a result, SOMs were generated with a large number of classes for specific months when model errors were found. With nearly ten years of forcing data, an adequate number of samples have been used to determine how cloud fraction varies across the SOM and to distinguish cloud errors. Barring major forcing errors, SCM studies can be thought of as what the GCM would simulate if the dynamics were perfect. As a result, simulated and observed CFs frequently occur for the same atmospheric states. For example, physically deep clouds during the winter months occur for a small number of classes in the SOM. Although the model produces clouds during the correct states, CFs are consistently too low. Instead, the model has a positive bias of thinner clouds during these classes that were associated with low-pressure systems and fronts. To determine if this and other SCM errors are present in the GCM, the Atmospheric Model Intercomparison Project (AMIP) run for the NASA GISS GCM will also be investigated. The SOM will be used to classify atmospheric states within the GCM to determine how well the GCM captures the PDF of observed atmospheric states. Together, these comparisons will allow for a thorough evaluation of the model at the ARM SGP site.

Feasibility of sea ice typing with synthetic aperture radar (SAR): Merging of Landsat thematic mapper and ERS 1 SAR satellite imagery

NASA Technical Reports Server (NTRS)

Steffen, Konrad; Heinrichs, John

1994-01-01

Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and Landsat thematic mapper (TM) images were acquired for the same area in the Beaufort Sea, April 16 and 18, 1992. The two image pairs were colocated to the same grid (25-m resolution), and a supervised ice type classification was performed on the TM images in order to classify ice free, nilas, gray ice, gray-white ice, thin first-year ice, medium and thick first-year ice, and old ice. Comparison of the collocated SAR pixels showed that ice-free areas can only be classified under calm wind conditions (less than 3 m/s) and for surface winds greater than 10 m/s based on the backscattering coefficient alone. This is true for pack ice regions during the cold months of the year where ice-free areas are spatially limited and where the capillary waves that cause SAR backscatter are dampened by entrained ice crystals. For nilas, two distinct backscatter classes were found at -17 dB and at -10 dB. The higher backscattering coefficient is attributed to the presence of frost flowers on light nilas. Gray and gray-white ice have a backscatter signature similar to first-year ice and therefore cannot be distinguished by SAR alone. First-year and old ice can be clearly separated based on their backscattering coefficient. The performance of the Geophysical Processor System ice classifier was tested against the Landsat derived ice products. It was found that smooth first-year ice and rough first-year ice were not significantly different in the backscatter domain. Ice concentration estimates based on ERS 1 C band SAR showed an error range of 5 to 8% for high ice concentration regions, mainly due to misclassified ice-free and smooth first-year ice areas. This error is expected to increase for areas of lower ice concentration. The combination of C band SAR and TM channels 2, 4, and 6 resulted in ice typing performance with an estimated accuracy of 90% for all seven ice classes.

Sub-pixel image classification for forest types in East Texas

NASA Astrophysics Data System (ADS)

Westbrook, Joey

Sub-pixel classification is the extraction of information about the proportion of individual materials of interest within a pixel. Landcover classification at the sub-pixel scale provides more discrimination than traditional per-pixel multispectral classifiers for pixels where the material of interest is mixed with other materials. It allows for the un-mixing of pixels to show the proportion of each material of interest. The materials of interest for this study are pine, hardwood, mixed forest and non-forest. The goal of this project was to perform a sub-pixel classification, which allows a pixel to have multiple labels, and compare the result to a traditional supervised classification, which allows a pixel to have only one label. The satellite image used was a Landsat 5 Thematic Mapper (TM) scene of the Stephen F. Austin Experimental Forest in Nacogdoches County, Texas and the four cover type classes are pine, hardwood, mixed forest and non-forest. Once classified, a multi-layer raster datasets was created that comprised four raster layers where each layer showed the percentage of that cover type within the pixel area. Percentage cover type maps were then produced and the accuracy of each was assessed using a fuzzy error matrix for the sub-pixel classifications, and the results were compared to the supervised classification in which a traditional error matrix was used. The overall accuracy of the sub-pixel classification using the aerial photo for both training and reference data had the highest (65% overall) out of the three sub-pixel classifications. This was understandable because the analyst can visually observe the cover types actually on the ground for training data and reference data, whereas using the FIA (Forest Inventory and Analysis) plot data, the analyst must assume that an entire pixel contains the exact percentage of a cover type found in a plot. An increase in accuracy was found after reclassifying each sub-pixel classification from nine classes with 10 percent interval each to five classes with 20 percent interval each. When compared to the supervised classification which has a satisfactory overall accuracy of 90%, none of the sub-pixel classification achieved the same level. However, since traditional per-pixel classifiers assign only one label to pixels throughout the landscape while sub-pixel classifications assign multiple labels to each pixel, the traditional 85% accuracy of acceptance for pixel-based classifications should not apply to sub-pixel classifications. More research is needed in order to define the level of accuracy that is deemed acceptable for sub-pixel classifications.

Frequency of pediatric medication administration errors and contributing factors.

PubMed

Ozkan, Suzan; Kocaman, Gulseren; Ozturk, Candan; Seren, Seyda

2011-01-01

This study examined the frequency of pediatric medication administration errors and contributing factors. This research used the undisguised observation method and Critical Incident Technique. Errors and contributing factors were classified through the Organizational Accident Model. Errors were made in 36.5% of the 2344 doses that were observed. The most frequent errors were those associated with administration at the wrong time. According to the results of this study, errors arise from problems within the system.

Heft Lemisphere: Exchanges Predominate in Segmental Speech Errors

ERIC Educational Resources Information Center

Nooteboom, Sieb G.; Quene, Hugo

2013-01-01

In most collections of segmental speech errors, exchanges are less frequent than anticipations and perseverations. However, it has been suggested that in inner speech exchanges might be more frequent than either anticipations or perseverations, because many half-way repaired errors (Yew...uhh...New York) are classified as repaired anticipations,…

Error Pattern Analysis Applied to Technical Writing: An Editor's Guide for Writers.

ERIC Educational Resources Information Center

Monagle, E. Brette

The use of error pattern analysis can reduce the time and money spent on editing and correcting manuscripts. What is required is noting, classifying, and keeping a frequency count of errors. First an editor should take a typical page of writing and circle each error. After the editor has done a sufficiently large number of pages to identify an…

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Aircrew perceived stress: examining crew performance, crew position and captains personality.

PubMed

Bowles, S; Ursin, H; Picano, J

2000-11-01

This study was conducted at NASA Ames Research Center as a part of a larger research project assessing the impact of captain's personality on crew performance and perceived stress in 24 air transport crews (5). Three different personality types for captains were classified based on a previous cluster analysis (3). Crews were comprised of three crewmembers: captain, first officer, and second officer/flight engineer. A total of 72 pilots completed a 1.5-d full-mission simulation of airline operations including emergency situations in the Ames Manned Vehicle System Research Facility B-727 simulator. Crewmembers were tested for perceived stress on four dimensions of the NASA Task Load Index after each of five flight legs. Crews were divided into three groups based on rankings from combined error and rating scores. High performance crews (who committed the least errors in flight) reported experiencing less stress in simulated flight than either low or medium crews. When comparing crew positions for perceived stress over all the simulated flights no significant differences were found. However, the crews led by the "Right Stuff" (e.g., active, warm, confident, competitive, and preferring excellence and challenges) personality type captains typically reported less stress than crewmembers led by other personality types.

Detecting Paroxysmal Coughing from Pertussis Cases Using Voice Recognition Technology

PubMed Central

Parker, Danny; Picone, Joseph; Harati, Amir; Lu, Shuang; Jenkyns, Marion H.; Polgreen, Philip M.

2013-01-01

Background Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. Methods We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. Results After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. Conclusion Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms. PMID:24391730

Speech Errors in Progressive Non-Fluent Aphasia

ERIC Educational Resources Information Center

Ash, Sharon; McMillan, Corey; Gunawardena, Delani; Avants, Brian; Morgan, Brianna; Khan, Alea; Moore, Peachie; Gee, James; Grossman, Murray

2010-01-01

The nature and frequency of speech production errors in neurodegenerative disease have not previously been precisely quantified. In the present study, 16 patients with a progressive form of non-fluent aphasia (PNFA) were asked to tell a story from a wordless children's picture book. Errors in production were classified as either phonemic,…

Tailoring a Human Reliability Analysis to Your Industry Needs

NASA Technical Reports Server (NTRS)

DeMott, D. L.

2016-01-01

Companies at risk of accidents caused by human error that result in catastrophic consequences include: airline industry mishaps, medical malpractice, medication mistakes, aerospace failures, major oil spills, transportation mishaps, power production failures and manufacturing facility incidents. Human Reliability Assessment (HRA) is used to analyze the inherent risk of human behavior or actions introducing errors into the operation of a system or process. These assessments can be used to identify where errors are most likely to arise and the potential risks involved if they do occur. Using the basic concepts of HRA, an evolving group of methodologies are used to meet various industry needs. Determining which methodology or combination of techniques will provide a quality human reliability assessment is a key element to developing effective strategies for understanding and dealing with risks caused by human errors. There are a number of concerns and difficulties in "tailoring" a Human Reliability Assessment (HRA) for different industries. Although a variety of HRA methodologies are available to analyze human error events, determining the most appropriate tools to provide the most useful results can depend on industry specific cultures and requirements. Methodology selection may be based on a variety of factors that include: 1) how people act and react in different industries, 2) expectations based on industry standards, 3) factors that influence how the human errors could occur such as tasks, tools, environment, workplace, support, training and procedure, 4) type and availability of data, 5) how the industry views risk & reliability, and 6) types of emergencies, contingencies and routine tasks. Other considerations for methodology selection should be based on what information is needed from the assessment. If the principal concern is determination of the primary risk factors contributing to the potential human error, a more detailed analysis method may be employed versus a requirement to provide a numerical value as part of a probabilistic risk assessment. Industries involved with humans operating large equipment or transport systems (ex. railroads or airlines) would have more need to address the man machine interface than medical workers administering medications. Human error occurs in every industry; in most cases the consequences are relatively benign and occasionally beneficial. In cases where the results can have disastrous consequences, the use of Human Reliability techniques to identify and classify the risk of human errors allows a company more opportunities to mitigate or eliminate these types of risks and prevent costly tragedies.

Common but unappreciated sources of error in one, two, and multiple-color pyrometry

NASA Technical Reports Server (NTRS)

Spjut, R. Erik

1988-01-01

The most common sources of error in optical pyrometry are examined. They can be classified as either noise and uncertainty errors, stray radiation errors, or speed-of-response errors. Through judicious choice of detectors and optical wavelengths the effect of noise errors can be minimized, but one should strive to determine as many of the system properties as possible. Careful consideration of the optical-collection system can minimize stray radiation errors. Careful consideration must also be given to the slowest elements in a pyrometer when measuring rapid phenomena.

Evaluation of exome variants using the Ion Proton Platform to sequence error-prone regions.

PubMed

Seo, Heewon; Park, Yoomi; Min, Byung Joo; Seo, Myung Eui; Kim, Ju Han

2017-01-01

The Ion Proton sequencer from Thermo Fisher accurately determines sequence variants from target regions with a rapid turnaround time at a low cost. However, misleading variant-calling errors can occur. We performed a systematic evaluation and manual curation of read-level alignments for the 675 ultrarare variants reported by the Ion Proton sequencer from 27 whole-exome sequencing data but that are not present in either the 1000 Genomes Project and the Exome Aggregation Consortium. We classified positive variant calls into 393 highly likely false positives, 126 likely false positives, and 156 likely true positives, which comprised 58.2%, 18.7%, and 23.1% of the variants, respectively. We identified four distinct error patterns of variant calling that may be bioinformatically corrected when using different strategies: simplicity region, SNV cluster, peripheral sequence read, and base inversion. Local de novo assembly successfully corrected 201 (38.7%) of the 519 highly likely or likely false positives. We also demonstrate that the two sequencing kits from Thermo Fisher (the Ion PI Sequencing 200 kit V3 and the Ion PI Hi-Q kit) exhibit different error profiles across different error types. A refined calling algorithm with better polymerase may improve the performance of the Ion Proton sequencing platform.

Emitter location errors in electronic recognition system

NASA Astrophysics Data System (ADS)

Matuszewski, Jan; Dikta, Anna

2017-04-01

The paper describes some of the problems associated with emitter location calculations. This aspect is the most important part of the series of tasks in the electronic recognition systems. The basic tasks include: detection of emission of electromagnetic signals, tracking (determining the direction of emitter sources), signal analysis in order to classify different emitter types and the identification of the sources of emission of the same type. The paper presents a brief description of the main methods of emitter localization and the basic mathematical formulae for calculating their location. The errors' estimation has been made to determine the emitter location for three different methods and different scenarios of emitters and direction finding (DF) sensors deployment in the electromagnetic environment. The emitter has been established using a special computer program. On the basis of extensive numerical calculations, the evaluation of precise emitter location in the recognition systems for different configuration alignment of bearing devices and emitter was conducted. The calculations which have been made based on the simulated data for different methods of location are presented in the figures and respective tables. The obtained results demonstrate that calculation of the precise emitter location depends on: the number of DF sensors, the distances between emitter and DF sensors, their mutual location in the reconnaissance area and bearing errors. The precise emitter location varies depending on the number of obtained bearings. The higher the number of bearings, the better the accuracy of calculated emitter location in spite of relatively high bearing errors for each DF sensor.

A new approach to identify, classify and count drugrelated events

PubMed Central

Bürkle, Thomas; Müller, Fabian; Patapovas, Andrius; Sonst, Anja; Pfistermeister, Barbara; Plank-Kiegele, Bettina; Dormann, Harald; Maas, Renke

2013-01-01

Aims The incidence of clinical events related to medication errors and/or adverse drug reactions reported in the literature varies by a degree that cannot solely be explained by the clinical setting, the varying scrutiny of investigators or varying definitions of drug-related events. Our hypothesis was that the individual complexity of many clinical cases may pose relevant limitations for current definitions and algorithms used to identify, classify and count adverse drug-related events. Methods Based on clinical cases derived from an observational study we identified and classified common clinical problems that cannot be adequately characterized by the currently used definitions and algorithms. Results It appears that some key models currently used to describe the relation of medication errors (MEs), adverse drug reactions (ADRs) and adverse drug events (ADEs) can easily be misinterpreted or contain logical inconsistencies that limit their accurate use to all but the simplest clinical cases. A key limitation of current models is the inability to deal with complex interactions such as one drug causing two clinically distinct side effects or multiple drugs contributing to a single clinical event. Using a large set of clinical cases we developed a revised model of the interdependence between MEs, ADEs and ADRs and extended current event definitions when multiple medications cause multiple types of problems. We propose algorithms that may help to improve the identification, classification and counting of drug-related events. Conclusions The new model may help to overcome some of the limitations that complex clinical cases pose to current paper- or software-based drug therapy safety. PMID:24007453

Individualised training to address variability of radiologists' performance

NASA Astrophysics Data System (ADS)

Sun, Shanghua; Taylor, Paul; Wilkinson, Louise; Khoo, Lisanne

2008-03-01

Computer-based tools are increasingly used for training and the continuing professional development of radiologists. We propose an adaptive training system to support individualised learning in mammography, based on a set of real cases, which are annotated with educational content by experienced breast radiologists. The system has knowledge of the strengths and weakness of each radiologist's performance: each radiologist is assessed to compute a profile showing how they perform on different sets of cases, classified by type of abnormality, breast density, and perceptual difficulty. We also assess variability in cognitive aspects of image perception, classifying errors made by radiologists as errors of search, recognition or decision. This is a novel element in our approach. The profile is used to select cases to present to the radiologist. The intelligent and flexible presentation of these cases distinguishes our system from existing training tools. The training cases are organised and indexed by an ontology we have developed for breast radiologist training, which is consistent with the radiologists' profile. Hence, the training system is able to select appropriate cases to compose an individualised training path, addressing the variability of the radiologists' performance. A substantial part of the system, the ontology has been evaluated on a large number of cases, and the training system is under implementation for further evaluation.

A novel evaluation of two related and two independent algorithms for eye movement classification during reading.

PubMed

Friedman, Lee; Rigas, Ioannis; Abdulin, Evgeny; Komogortsev, Oleg V

2018-05-15

Nystrӧm and Holmqvist have published a method for the classification of eye movements during reading (ONH) (Nyström & Holmqvist, 2010). When we applied this algorithm to our data, the results were not satisfactory, so we modified the algorithm (now the MNH) to better classify our data. The changes included: (1) reducing the amount of signal filtering, (2) excluding a new type of noise, (3) removing several adaptive thresholds and replacing them with fixed thresholds, (4) changing the way that the start and end of each saccade was determined, (5) employing a new algorithm for detecting PSOs, and (6) allowing a fixation period to either begin or end with noise. A new method for the evaluation of classification algorithms is presented. It was designed to provide comprehensive feedback to an algorithm developer, in a time-efficient manner, about the types and numbers of classification errors that an algorithm produces. This evaluation was conducted by three expert raters independently, across 20 randomly chosen recordings, each classified by both algorithms. The MNH made many fewer errors in determining when saccades start and end, and it also detected some fixations and saccades that the ONH did not. The MNH fails to detect very small saccades. We also evaluated two additional algorithms: the EyeLink Parser and a more current, machine-learning-based algorithm. The EyeLink Parser tended to find more saccades that ended too early than did the other methods, and we found numerous problems with the output of the machine-learning-based algorithm.

Error reduction in EMG signal decomposition

PubMed Central

Kline, Joshua C.

2014-01-01

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization. PMID:25210159

Algorithmic Classification of Five Characteristic Types of Paraphasias.

PubMed

Fergadiotis, Gerasimos; Gorman, Kyle; Bedrick, Steven

2016-12-01

This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes.

Medication Errors: New EU Good Practice Guide on Risk Minimisation and Error Prevention.

PubMed

Goedecke, Thomas; Ord, Kathryn; Newbould, Victoria; Brosch, Sabine; Arlett, Peter

2016-06-01

A medication error is an unintended failure in the drug treatment process that leads to, or has the potential to lead to, harm to the patient. Reducing the risk of medication errors is a shared responsibility between patients, healthcare professionals, regulators and the pharmaceutical industry at all levels of healthcare delivery. In 2015, the EU regulatory network released a two-part good practice guide on medication errors to support both the pharmaceutical industry and regulators in the implementation of the changes introduced with the EU pharmacovigilance legislation. These changes included a modification of the 'adverse reaction' definition to include events associated with medication errors, and the requirement for national competent authorities responsible for pharmacovigilance in EU Member States to collaborate and exchange information on medication errors resulting in harm with national patient safety organisations. To facilitate reporting and learning from medication errors, a clear distinction has been made in the guidance between medication errors resulting in adverse reactions, medication errors without harm, intercepted medication errors and potential errors. This distinction is supported by an enhanced MedDRA(®) terminology that allows for coding all stages of the medication use process where the error occurred in addition to any clinical consequences. To better understand the causes and contributing factors, individual case safety reports involving an error should be followed-up with the primary reporter to gather information relevant for the conduct of root cause analysis where this may be appropriate. Such reports should also be summarised in periodic safety update reports and addressed in risk management plans. Any risk minimisation and prevention strategy for medication errors should consider all stages of a medicinal product's life-cycle, particularly the main sources and types of medication errors during product development. This article describes the key concepts of the EU good practice guidance for defining, classifying, coding, reporting, evaluating and preventing medication errors. This guidance should contribute to the safe and effective use of medicines for the benefit of patients and public health.

User intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis.

PubMed

Woodward, Richard B; Spanias, John A; Hargrove, Levi J

2016-08-01

Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming. By using subject independent datasets, whereby a unique subject is tested against a pooled dataset of other subjects, we believe subject training time can be reduced while still achieving an accurate classification. We present here an intent recognition system using an artificial neural network (ANN) with a scaled conjugate gradient learning algorithm to classify gait intention with user-dependent and independent datasets for six unilateral lower limb amputees. We compare these results against a linear discriminant analysis (LDA) classifier. The ANN was found to have significantly lower classification error (P<;0.05) than LDA with all user-dependent step-types, as well as transitional steps for user-independent datasets. Both types of classifiers are capable of making fast decisions; 1.29 and 2.83 ms for the LDA and ANN respectively. These results suggest that ANNs can provide suitable and accurate offline classification in prosthesis gait prediction.

Pre-University Students' Errors in Integration of Rational Functions and Implications for Classroom Teaching

ERIC Educational Resources Information Center

Yee, Ng Kin; Lam, Toh Tin

2008-01-01

This paper reports on students' errors in performing integration of rational functions, a topic of calculus in the pre-university mathematics classrooms. Generally the errors could be classified as those due to the students' weak algebraic concepts and their lack of understanding of the concept of integration. With the students' inability to link…

Secondary School Students' Errors in the Translation of Algebraic Statements

ERIC Educational Resources Information Center

Molina, Marta; Rodríguez-Domingo, Susana; Cañadas, María Consuelo; Castro, Encarnación

2017-01-01

In this article, we present the results of a research study that explores secondary students' capacity to perform translations of algebraic statements between the verbal and symbolic representation systems through the lens of errors. We classify and compare the errors made by 2 groups of students: 1 at the beginning of their studies in school…

What Are Error Rates for Classifying Teacher and School Performance Using Value-Added Models?

ERIC Educational Resources Information Center

Schochet, Peter Z.; Chiang, Hanley S.

2013-01-01

This article addresses likely error rates for measuring teacher and school performance in the upper elementary grades using value-added models applied to student test score gain data. Using a realistic performance measurement system scheme based on hypothesis testing, the authors develop error rate formulas based on ordinary least squares and…

Automatic classification for mammogram backgrounds based on bi-rads complexity definition and on a multi content analysis framework

NASA Astrophysics Data System (ADS)

Wu, Jie; Besnehard, Quentin; Marchessoux, Cédric

2011-03-01

Clinical studies for the validation of new medical imaging devices require hundreds of images. An important step in creating and tuning the study protocol is the classification of images into "difficult" and "easy" cases. This consists of classifying the image based on features like the complexity of the background, the visibility of the disease (lesions). Therefore, an automatic medical background classification tool for mammograms would help for such clinical studies. This classification tool is based on a multi-content analysis framework (MCA) which was firstly developed to recognize image content of computer screen shots. With the implementation of new texture features and a defined breast density scale, the MCA framework is able to automatically classify digital mammograms with a satisfying accuracy. BI-RADS (Breast Imaging Reporting Data System) density scale is used for grouping the mammograms, which standardizes the mammography reporting terminology and assessment and recommendation categories. Selected features are input into a decision tree classification scheme in MCA framework, which is the so called "weak classifier" (any classifier with a global error rate below 50%). With the AdaBoost iteration algorithm, these "weak classifiers" are combined into a "strong classifier" (a classifier with a low global error rate) for classifying one category. The results of classification for one "strong classifier" show the good accuracy with the high true positive rates. For the four categories the results are: TP=90.38%, TN=67.88%, FP=32.12% and FN =9.62%.

The maximum entropy method of moments and Bayesian probability theory

NASA Astrophysics Data System (ADS)

Bretthorst, G. Larry

2013-08-01

The problem of density estimation occurs in many disciplines. For example, in MRI it is often necessary to classify the types of tissues in an image. To perform this classification one must first identify the characteristics of the tissues to be classified. These characteristics might be the intensity of a T1 weighted image and in MRI many other types of characteristic weightings (classifiers) may be generated. In a given tissue type there is no single intensity that characterizes the tissue, rather there is a distribution of intensities. Often this distributions can be characterized by a Gaussian, but just as often it is much more complicated. Either way, estimating the distribution of intensities is an inference problem. In the case of a Gaussian distribution, one must estimate the mean and standard deviation. However, in the Non-Gaussian case the shape of the density function itself must be inferred. Three common techniques for estimating density functions are binned histograms [1, 2], kernel density estimation [3, 4], and the maximum entropy method of moments [5, 6]. In the introduction, the maximum entropy method of moments will be reviewed. Some of its problems and conditions under which it fails will be discussed. Then in later sections, the functional form of the maximum entropy method of moments probability distribution will be incorporated into Bayesian probability theory. It will be shown that Bayesian probability theory solves all of the problems with the maximum entropy method of moments. One gets posterior probabilities for the Lagrange multipliers, and, finally, one can put error bars on the resulting estimated density function.

A neural network for noise correlation classification

NASA Astrophysics Data System (ADS)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

PubMed

Gustafsson, Mats G; Wallman, Mikael; Wickenberg Bolin, Ulrika; Göransson, Hanna; Fryknäs, M; Andersson, Claes R; Isaksson, Anders

2010-06-01

Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (CI) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the CI is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice. It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples. Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets. An empirically derived ME prior seems promising for improving the Bayesian CI for the unknown error rate of a designed classifier. Copyright 2010 Elsevier B.V. All rights reserved.

On the statistical assessment of classifiers using DNA microarray data

PubMed Central

Ancona, N; Maglietta, R; Piepoli, A; D'Addabbo, A; Cotugno, R; Savino, M; Liuni, S; Carella, M; Pesole, G; Perri, F

2006-01-01

Background In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. Results We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed. Conclusions The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required. PMID:16919171

Wildlife management by habitat units: A preliminary plan of action

NASA Technical Reports Server (NTRS)

Frentress, C. D.; Frye, R. G.

1975-01-01

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia.

PubMed

Hüttich, Christian; Herold, Martin; Strohbach, Ben J; Dech, Stefan

2011-05-01

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat

PubMed Central

de Weerd, Nelleke; van Langevelde, Frank; van Oeveren, Herman; Nolet, Bart A.; Kölzsch, Andrea; Prins, Herbert H. T.; de Boer, W. Fred

2015-01-01

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level. PMID:26107643

Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat.

PubMed

de Weerd, Nelleke; van Langevelde, Frank; van Oeveren, Herman; Nolet, Bart A; Kölzsch, Andrea; Prins, Herbert H T; de Boer, W Fred

2015-01-01

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.

Effectiveness of Global Features for Automatic Medical Image Classification and Retrieval – the experiences of OHSU at ImageCLEFmed

PubMed Central

Kalpathy-Cramer, Jayashree; Hersh, William

2008-01-01

In 2006 and 2007, Oregon Health & Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the Cross Language Evaluation Forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the Image Retrieval in Medical Applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data. PMID:19884953

Evaluating data mining algorithms using molecular dynamics trajectories.

PubMed

Tatsis, Vasileios A; Tjortjis, Christos; Tzirakis, Panagiotis

2013-01-01

Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.

Forest cover type analysis of New England forests using innovative WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Kovacs, Jenna M.

For many years, remote sensing has been used to generate land cover type maps to create a visual representation of what is occurring on the ground. One significant use of remote sensing is the identification of forest cover types. New England forests are notorious for their especially complex forest structure and as a result have been, and continue to be, a challenge when classifying forest cover types. To most accurately depict forest cover types occurring on the ground, it is essential to utilize image data that have a suitable combination of both spectral and spatial resolution. The WorldView-2 (WV2) commercial satellite, launched in 2009, is the first of its kind, having both high spectral and spatial resolutions. WV2 records eight bands of multispectral imagery, four more than the usual high spatial resolution sensors, and has a pixel size of 1.85 meters at the nadir. These additional bands have the potential to improve classification detail and classification accuracy of forest cover type maps. For this reason, WV2 imagery was utilized on its own, and in combination with Landsat 5 TM (LS5) multispectral imagery, to evaluate whether these image data could more accurately classify forest cover types. In keeping with recent developments in image analysis, an Object-Based Image Analysis (OBIA) approach was used to segment images of Pawtuckaway State Park and nearby private lands, an area representative of the typical complex forest structure found in the New England region. A Classification and Regression Tree (CART) analysis was then used to classify image segments at two levels of classification detail. Accuracies for each forest cover type map produced were generated using traditional and area-based error matrices, and additional standard accuracy measures (i.e., KAPPA) were generated. The results from this study show that there is value in analyzing imagery with both high spectral and spatial resolutions, and that WV2's new and innovative bands can be useful for the classification of complex forest structures.

Analysis of DSN software anomalies

NASA Technical Reports Server (NTRS)

Galorath, D. D.; Hecht, H.; Hecht, M.; Reifer, D. J.

1981-01-01

A categorized data base of software errors which were discovered during the various stages of development and operational use of the Deep Space Network DSN/Mark 3 System was developed. A study team identified several existing error classification schemes (taxonomies), prepared a detailed annotated bibliography of the error taxonomy literature, and produced a new classification scheme which was tuned to the DSN anomaly reporting system and encapsulated the work of others. Based upon the DSN/RCI error taxonomy, error data on approximately 1000 reported DSN/Mark 3 anomalies were analyzed, interpreted and classified. Next, error data are summarized and histograms were produced highlighting key tendencies.

Optimizing pattern recognition-based control for partial-hand prosthesis application.

PubMed

Earley, Eric J; Adewuyi, Adenike A; Hargrove, Levi J

2014-01-01

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (p<0.001); and that training in only variable wrist positions, with only dynamic wrist movements, or with both variable wrist positions and movements results in lower error rates than training in only the neutral wrist position (p<0.001). Finally, our results show that both an increase in window length and a decrease in the number of grasps available to the classifier significantly decrease classification error (p<0.001). These results remained consistent whether the classifier selected or maintained a hand-grasp.

The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.

[A case of pure agraphia due to left parietal lobe infarction].

PubMed

Yaguchi, H; Bando, M; Kubo, T; Ohi, M; Suzuki, K

1998-06-01

We reported a case of a 63-year-old right handed man with pure agraphia due to the left parietal lobe infarction. The characteristics of agraphia in the patient were as follows. 1) The written letters were generally recognizable and well formed. 2) He succeeded in pointing to single Kana letter named by the examiner from the Japanese syllabary, but missed in pointing to Kana words. 3) Further, it took more time for the patient to point to even single Kana letter than for the control. 4) Most errors in Kana writing was substitution. Errors in Kanji writing are partial lacking or no response. But his ability in Kanji writing was facilitated by visual cues. He was unable to describe the Hen (a left-hand radical) and Tsukuri (the body) of some Kanji letters and to name some Kanji letters when their Hen and Tsukuri were orally given. We classified pure agraphia into two types out of some references. In one type (Type 1), letters in writing are poorly formed, but the ability to make words with the methods other than writing, for example spelling with anagrams or typing are preserved. In another type (Type 2), letters in writing were well-formed, but spelling with anagrams or typing were abnormal. Type 1 agraphia could result from the only deficit of graphic motor engram, while type 2 agraphia could be caused by the deficits other than graphic motor engram. Agraphia in this case belongs to the type 2. The features of agraphia in this case suggested that his agraphia was caused by a disorder in recalling graphemes of letters, and in arranging at least of Kana-letters.

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Evaluation of complications of root canal treatment performed by undergraduate dental students.

PubMed

AlRahabi, Mothanna K

2017-12-01

This study evaluated the technical quality of root canal treatment (RCT) and detected iatrogenic errors in an undergraduate dental clinic at the College of Dentistry, Taibah University, Saudi Arabia. Dental records of 280 patients who received RCT between 2013 and 2016 undertaken by dental students were investigated by retrospective chart review. Root canal obturation was evaluated on the basis of the length of obturation being ≤2 mm from the radiographic apex, with uniform radiodensity and good adaptation to root canal walls. Inadequate root canal obturation included cases containing procedural errors such as furcal perforation, ledge, canal transportation, strip perforation, root perforation, instrument separation, voids in the obturation, or underfilling or overfilling of the obturation. In 193 (68.9%) teeth, RCT was adequate and without procedural errors. However, in 87 (31.1%) teeth, RCT was inadequate and contained procedural errors. The frequency of procedural errors in the entire sample was 31.1% as follows: underfilling, 49.9%; overfilling, 24.1%; voids, 12.6%; broken instruments, 9.2%; apical perforation, 2.3%; and root canal transportation, 2.3%. There were no significant differences (p > 0.05) in the type or frequency of procedural errors between the fourth- and fifth-year students. Lower molars (43.1%) and upper incisors (19.2%) exhibited the highest and lowest frequencies of procedural errors, respectively. The technical quality of RCT performed by undergraduate dental students was classified as 'adequate' in 68.9% of the cases. There is a need for improvement in the training of students at the preclinical and clinical levels.

Automated Classification of Phonological Errors in Aphasic Language

PubMed Central

Ahuja, Sanjeev B.; Reggia, James A.; Berndt, Rita S.

1984-01-01

Using heuristically-guided state space search, a prototype program has been developed to simulate and classify phonemic errors occurring in the speech of neurologically-impaired patients. Simulations are based on an interchangeable rule/operator set of elementary errors which represent a theory of phonemic processing faults. This work introduces and evaluates a novel approach to error simulation and classification, it provides a prototype simulation tool for neurolinguistic research, and it forms the initial phase of a larger research effort involving computer modelling of neurolinguistic processes.

Physicians and pharmacists: collaboration to improve the quality of prescriptions in primary care in Mexico.

PubMed

Mino-León, Dolores; Reyes-Morales, Hortensia; Jasso, Luis; Douvoba, Svetlana Vladislavovna

2012-06-01

Inappropriate prescription is a relevant problem in primary health care settings in Mexico, with potentially harmful consequences for patients. To evaluate the effectiveness of incorporating a pharmacist into primary care health team to reduce prescription errors for patients with diabetes and/or hypertension. One Family Medicine Clinic from the Mexican Institute of Social Security in Mexico City. A "pharmacotherapy intervention" provided by pharmacists through a quasi experimental (before-after) design was carried out. Physicians who allowed access to their diabetes and/or hypertensive patients' medical records and prescriptions were included in the study. Prescription errors were classified as "filling", "clinical" or "both". Descriptive analysis, identification of potential drug-drug interactions (pD-DI), and comparison of the proportion of patients with prescriptions with errors detected "before" and "after" intervention were performed. Decrease in the proportion of patients who received prescriptions with errors after the intervention. Pharmacists detected at least one type of error in 79 out of 160 patients. Errors were "clinical", "both" and "filling" in 47, 21 and 11 of these patient's prescriptions respectively. Predominant errors were, in the subgroup of patient's prescriptions with "clinical" errors, pD-DI; in the subgroup of "both" errors, lack of information on dosing interval and pD-DI; and in the "filling" subgroup, lack of information on dosing interval. The pD-DI caused 50 % of the errors detected, from which 19 % were of major severity. The impact of the correction of errors post-intervention was observed in 19 % of patients who had erroneous prescriptions before the intervention of the pharmacist (49.3-30.3 %, p < 0.05). The impact of the intervention was relevant from a clinical point of view for the public health services in Mexico. The implementation of early warning systems of the most widely prescribed drugs is an alternative for reducing prescription errors and consequently the risks they may cause.

Interventions to reduce medication errors in neonatal care: a systematic review

PubMed Central

Nguyen, Minh-Nha Rhylie; Mosel, Cassandra

2017-01-01

Background: Medication errors represent a significant but often preventable cause of morbidity and mortality in neonates. The objective of this systematic review was to determine the effectiveness of interventions to reduce neonatal medication errors. Methods: A systematic review was undertaken of all comparative and noncomparative studies published in any language, identified from searches of PubMed and EMBASE and reference-list checking. Eligible studies were those investigating the impact of any medication safety interventions aimed at reducing medication errors in neonates in the hospital setting. Results: A total of 102 studies were identified that met the inclusion criteria, including 86 comparative and 16 noncomparative studies. Medication safety interventions were classified into six themes: technology (n = 38; e.g. electronic prescribing), organizational (n = 16; e.g. guidelines, policies, and procedures), personnel (n = 13; e.g. staff education), pharmacy (n = 9; e.g. clinical pharmacy service), hazard and risk analysis (n = 8; e.g. error detection tools), and multifactorial (n = 18; e.g. any combination of previous interventions). Significant variability was evident across all included studies, with differences in intervention strategies, trial methods, types of medication errors evaluated, and how medication errors were identified and evaluated. Most studies demonstrated an appreciable risk of bias. The vast majority of studies (>90%) demonstrated a reduction in medication errors. A similar median reduction of 50–70% in medication errors was evident across studies included within each of the identified themes, but findings varied considerably from a 16% increase in medication errors to a 100% reduction in medication errors. Conclusion: While neonatal medication errors can be reduced through multiple interventions aimed at improving the medication use process, no single intervention appeared clearly superior. Further research is required to evaluate the relative cost-effectiveness of the various medication safety interventions to facilitate decisions regarding uptake and implementation into clinical practice. PMID:29387337

Text Classification for Assisting Moderators in Online Health Communities

PubMed Central

Huh, Jina; Yetisgen-Yildiz, Meliha; Pratt, Wanda

2013-01-01

Objectives Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators’ help. Methods We employed a binary classifier on WebMD’s online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. Results Using sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients’ posts. Discussion We showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members’ needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges. Conclusion Social media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators’ expertise in these large-scale, social media environments. PMID:24025513

Utilizing spatial and spectral features of photoacoustic imaging for ovarian cancer detection and diagnosis

NASA Astrophysics Data System (ADS)

Li, Hai; Kumavor, Patrick; Salman Alqasemi, Umar; Zhu, Quing

2015-01-01

A composite set of ovarian tissue features extracted from photoacoustic spectral data, beam envelope, and co-registered ultrasound and photoacoustic images are used to characterize malignant and normal ovaries using logistic and support vector machine (SVM) classifiers. Normalized power spectra were calculated from the Fourier transform of the photoacoustic beamformed data, from which the spectral slopes and 0-MHz intercepts were extracted. Five features were extracted from the beam envelope and another 10 features were extracted from the photoacoustic images. These 17 features were ranked by their p-values from t-tests on which a filter type of feature selection method was used to determine the optimal feature number for final classification. A total of 169 samples from 19 ex vivo ovaries were randomly distributed into training and testing groups. Both classifiers achieved a minimum value of the mean misclassification error when the seven features with lowest p-values were selected. Using these seven features, the logistic and SVM classifiers obtained sensitivities of 96.39±3.35% and 97.82±2.26%, and specificities of 98.92±1.39% and 100%, respectively, for the training group. For the testing group, logistic and SVM classifiers achieved sensitivities of 92.71±3.55% and 92.64±3.27%, and specificities of 87.52±8.78% and 98.49±2.05%, respectively.

Bayes classification of terrain cover using normalized polarimetric data

NASA Technical Reports Server (NTRS)

Yueh, H. A.; Swartz, A. A.; Kong, J. A.; Shin, R. T.; Novak, L. M.

1988-01-01

The normalized polarimetric classifier (NPC) which uses only the relative magnitudes and phases of the polarimetric data is proposed for discrimination of terrain elements. The probability density functions (PDFs) of polarimetric data are assumed to have a complex Gaussian distribution, and the marginal PDF of the normalized polarimetric data is derived by adopting the Euclidean norm as the normalization function. The general form of the distance measure for the NPC is also obtained. It is demonstrated that for polarimetric data with an arbitrary PDF, the distance measure of NPC will be independent of the normalization function selected even when the classifier is mistrained. A complex Gaussian distribution is assumed for the polarimetric data consisting of grass and tree regions. The probability of error for the NPC is compared with those of several other single-feature classifiers. The classification error of NPCs is shown to be independent of the normalization function.

Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Embedded feature ranking for ensemble MLP classifiers.

PubMed

Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond

2011-06-01

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

A framework for software fault tolerance in real-time systems

NASA Technical Reports Server (NTRS)

Anderson, T.; Knight, J. C.

1983-01-01

A classification scheme for errors and a technique for the provision of software fault tolerance in cyclic real-time systems is presented. The technique requires that the process structure of a system be represented by a synchronization graph which is used by an executive as a specification of the relative times at which they will communicate during execution. Communication between concurrent processes is severely limited and may only take place between processes engaged in an exchange. A history of error occurrences is maintained by an error handler. When an error is detected, the error handler classifies it using the error history information and then initiates appropriate recovery action.

A theory of human error

NASA Technical Reports Server (NTRS)

Mcruer, D. T.; Clement, W. F.; Allen, R. W.

1980-01-01

Human error, a significant contributing factor in a very high proportion of civil transport, general aviation, and rotorcraft accidents is investigated. Correction of the sources of human error requires that one attempt to reconstruct underlying and contributing causes of error from the circumstantial causes cited in official investigative reports. A validated analytical theory of the input-output behavior of human operators involving manual control, communication, supervisory, and monitoring tasks which are relevant to aviation operations is presented. This theory of behavior, both appropriate and inappropriate, provides an insightful basis for investigating, classifying, and quantifying the needed cause-effect relationships governing propagation of human error.

Automatic classification of background EEG activity in healthy and sick neonates

NASA Astrophysics Data System (ADS)

Löfhede, Johan; Thordstein, Magnus; Löfgren, Nils; Flisberg, Anders; Rosa-Zurera, Manuel; Kjellmer, Ingemar; Lindecrantz, Kaj

2010-02-01

The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

[A new method for the classification of neonates based on maturity and somatic development].

PubMed

Berkö, P

1992-03-01

The author points out the sad fact that the methods of estimating classifying, comparative examining of the maturity, somatic development of newborns and the methods of marking off the retarded newborns used up to now are essentially centering round the birth-weight. He exposes the errors, deficiencies of these methods and confronts them with the possibilities of the NDN-system (newborn's somatic development and nutritional state) worked out earlier by him. He thinks the NDF-system to be able to express simultaneously and exactly the gestational age, weight- and length-development, state of being fed of the newborns and the relation to the populational average, the fact and type of retardation, as well. The informational means of NDF-system in NDF-index. The NDF-system makes it possible to break down the birth-weight centric view as it offers a more suitable and qualified method than used before to describe the maturity and somatic development and the classifying of the newborns on the basis of these.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

Utilizing LANDSAT imagery to monitor land-use change - A case study in Ohio

NASA Technical Reports Server (NTRS)

Gordon, S. I.

1980-01-01

A study, performed in Ohio, of the nature and extent of interpretation errors in the application of Landsat imagery to land-use planning and modeling is reported. Potential errors associated with the misalignment of pixels after geometric correction and with misclassification of land cover or land use due to spectral similarities were identified on interpreted computer-compatible tapes of a portion of Franklin County for two adjacent days of 1975 and one day of 1973, and the extents of these errors were quantified by comparison with a ground-checked set of aerial-photograph interpretations. The open-space and agricultural categories are found to be the most consistently classified, while the more urban areas were classified correctly only from about 43 to 8% of the time. It is thus recommended that the direct application of Landsat data to land-use planning must await improvements in classification techniques and accuracy.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

46 CFR 108.187 - Ventilation for brush type electric motors in classified spaces.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 4 2011-10-01 2011-10-01 false Ventilation for brush type electric motors in classified... Ventilation for brush type electric motors in classified spaces. Ventilation for brush type electric motors in classified locations must meet N.F.P.A. 496-1974 “Standard for Purged and Pressurized Enclosures for...

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

NASA Astrophysics Data System (ADS)

Sreejith, Sreevarsha; Pereverzyev, Sergiy, Jr.; Kelvin, Lee S.; Marleau, Francine R.; Haltmeier, Markus; Ebner, Judith; Bland-Hawthorn, Joss; Driver, Simon P.; Graham, Alister W.; Holwerda, Benne W.; Hopkins, Andrew M.; Liske, Jochen; Loveday, Jon; Moffett, Amanda J.; Pimbblet, Kevin A.; Taylor, Edward N.; Wang, Lingyu; Wright, Angus H.

2018-03-01

We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters, and stellar mass), we apply the techniques of Support Vector Machines, Classification Trees, Classification Trees with Random Forest (CTRF) and Neural Networks, and returning True Prediction Ratios (TPRs) of 75.8 per cent, 69.0 per cent, 76.2 per cent, and 76.0 per cent, respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification (`unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ˜ 9 per cent of ellipticals, ˜ 9 per cent of little blue spheroids, ˜ 14 per cent of early-type spirals, ˜ 21 per cent of intermediate-type spirals, and ˜ 4 per cent of late-type spirals and irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy data sets. Adopting the CTRF algorithm, the TPRs of the five galaxy types are : E, 70.1 per cent; LBS, 75.6 per cent; S0-Sa, 63.6 per cent; Sab-Scd, 56.4 per cent, and Sd-Irr, 88.9 per cent. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS, and S0-Sa) and disc-dominated (Sab-Scd and Sd-Irr), achieving an overall accuracy of 89.8 per cent. This translates into an accuracy of 84.9 per cent for spheroid-dominated systems and 92.5 per cent for disc-dominated systems.

Improving UK Air Quality Modelling Through Exploitation of Satellite Observations

NASA Astrophysics Data System (ADS)

Pope, Richard; Chipperfield, Martyn; Savage, Nick

2014-05-01

In this work the applicability of satellite observations to evaluate the operational UK Met Office Air Quality in the Unified Model (AQUM) have been investigated. The main focus involved the AQUM validation against satellite observations, investigation of satellite retrieval error types and of synoptic meteorological-atmospheric chemistry relationships simulated/seen by the AQUM/satellite. The AQUM is a short range forecast model of atmospheric chemistry and aerosols up to 5 days. It has been designed to predict potentially hazardous air pollution events, e.g. high concentrations of surface ozone. The AQUM has only been validated against UK atmospheric chemistry recording surface stations. Therefore, satellite observations of atmospheric chemistry have been used to further validate the model, taking advantage of better satellite spatial coverage. Observations of summer and winter 2006 tropospheric column NO2 from both OMI and SCIAMACHY show that the AQUM generally compares well with the observations. However, in northern England positive biases (AQUM - satellite) suggest that the AQUM overestimates column NO2; we present results of sensitivity experiments on UK emissions datasets suspected to be the cause. In winter, the AQUM over predicts background column NO2 when compared to both satellite instruments. We hypothesise that the cause is the AQUM winter night-time chemistry, where the NO2 sinks are not substantially defined. Satellite data are prone to errors/uncertainty such as random, systematic and smoothing errors. We have investigated these error types and developed an algorithm to calculate and reduce the random error component of DOAS NO2 retrievals, giving more robust seasonal satellite composites. The Lamb Weather Types (LWT), an objective method of classifying the daily synoptic weather over the UK, were used to create composite satellite maps of column NO2 under different synoptic conditions. Under cyclonic conditions, satellite observed UK column NO2 is reduced as the indicative south-westerly flow transports it away from the UK over the North Sea. However, under anticyclonic conditions, the satellite shows that the stable conditions enhance the build-up of column NO2 over source regions. The influence of wind direction on column NO2 can also be seen from space with transport leeward of the source regions.

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Assessing the statistical significance of the achieved classification error of classifiers constructed using serum peptide profiles, and a prescription for random sampling repeated studies for massive high-throughput genomic and proteomic studies.

PubMed

Lyons-Weiler, James; Pelikan, Richard; Zeh, Herbert J; Whitcomb, David C; Malehorn, David E; Bigbee, William L; Hauskrecht, Milos

2005-01-01

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE.

Comparison of Meropenem MICs and Susceptibilities for Carbapenemase-Producing Klebsiella pneumoniae Isolates by Various Testing Methods▿

PubMed Central

Bulik, Catharine C.; Fauntleroy, Kathy A.; Jenkins, Stephen G.; Abuali, Mayssa; LaBombardi, Vincent J.; Nicolau, David P.; Kuti, Joseph L.

2010-01-01

We describe the levels of agreement between broth microdilution, Etest, Vitek 2, Sensititre, and MicroScan methods to accurately define the meropenem MIC and categorical interpretation of susceptibility against carbapenemase-producing Klebsiella pneumoniae (KPC). A total of 46 clinical K. pneumoniae isolates with KPC genotypes, all modified Hodge test and blaKPC positive, collected from two hospitals in NY were included. Results obtained by each method were compared with those from broth microdilution (the reference method), and agreement was assessed based on MICs and Clinical Laboratory Standards Institute (CLSI) interpretative criteria using 2010 susceptibility breakpoints. Based on broth microdilution, 0%, 2.2%, and 97.8% of the KPC isolates were classified as susceptible, intermediate, and resistant to meropenem, respectively. Results from MicroScan demonstrated the most agreement with those from broth microdilution, with 95.6% agreement based on the MIC and 2.2% classified as minor errors, and no major or very major errors. Etest demonstrated 82.6% agreement with broth microdilution MICs, a very major error rate of 2.2%, and a minor error rate of 2.2%. Vitek 2 MIC agreement was 30.4%, with a 23.9% very major error rate and a 39.1% minor error rate. Sensititre demonstrated MIC agreement for 26.1% of isolates, with a 3% very major error rate and a 26.1% minor error rate. Application of FDA breakpoints had little effect on minor error rates but increased very major error rates to 58.7% for Vitek 2 and Sensititre. Meropenem MIC results and categorical interpretations for carbapenemase-producing K. pneumoniae differ by methodology. Confirmation of testing results is encouraged when an accurate MIC is required for antibiotic dosing optimization. PMID:20484603

Diagnosis of Cognitive Errors by Statistical Pattern Recognition Methods.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi K.; Tatsuoka, Maurice M.

The rule space model permits measurement of cognitive skill acquisition, diagnosis of cognitive errors, and detection of the strengths and weaknesses of knowledge possessed by individuals. Two ways to classify an individual into his or her most plausible latent state of knowledge include: (1) hypothesis testing--Bayes' decision rules for minimum…

The impact of OCR accuracy on automated cancer classification of pathology reports.

PubMed

Zuccon, Guido; Nguyen, Anthony N; Bergheim, Anton; Wickman, Sandra; Grayson, Narelle

2012-01-01

To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

An extension of the receiver operating characteristic curve and AUC-optimal classification.

PubMed

Takenouchi, Takashi; Komori, Osamu; Eguchi, Shinto

2012-10-01

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

Interaction of clothing and body mass index affects validity of air-displacement plethysmography in adults.

PubMed

Shafer, Kimberly J; Siders, William A; Johnson, LuAnn K; Lukaski, Henry C

2008-02-01

We determined the effect of clothing type on the validity of air-displacement plethysmography (ADP) to estimate percentage of body fat (%BF) and ascertain if these effects differ by body mass index (BMI). The %BF by dual x-ray absorptiometry (DXA) and %BF, density, and body volume by ADP were assessed in 132 healthy adults classified by normal (N; 18.5-24.9 kg/m2), overweight (OW; 25-29.9 kg/m2), and obese (OB; 30-39.9 kg/m2) BMIs. Compared with DXA, ADP underestimated (P < 0.0001) %BF from scrubs (SC) and t-shirt/shorts (TS) in N (11.4%; 8.6%) and OW (6.8%; 4.9%) BMI groups, respectively. ADP compared with DXA overestimated (P < 0.0006) %BF in the OW group (1.2%), but underestimated (P < 0.0001) it in the N group (2.4%). ADP also overestimated (P < 0.006) %BF in the OB group wearing spandex (SP; 4.8%), but not in those wearing SC (0.7%; P = 0.10) and TS (0.5%; P = 0.22) versus DXA. All three clothing types showed significant error in estimating %BF with ADP compared with DXA in N and OW BMI. Use of spandex provided the least error and is the preferred attire to obtain valid body composition results when testing N and OW subjects. However, SP provided the greatest error in the OB group. Error in ADP %BF in OB was minimal in SC and TS and similar to the within-subject variability in %BF estimates with ADP. Thus, TS and SC are acceptable alternatives to SP in adults with excess body weight.

Single Event Effect Testing of the Micron MT46V128M8

NASA Technical Reports Server (NTRS)

Stansberry, Scott; Campola, Michael; Wilcox, Ted; Seidleck, Christina; Phan, Anthony

2017-01-01

The Micron MT46V128M8 was tested for single event effects (SEE) at the Texas AM University Cyclotron Facility (TAMU) in June of 2017. Testing revealed a sensitivity to device hang-ups classified as single event functional interrupts (SEFI) and possible soft data errors classified as single event upsets (SEU).

Quantum biological channel modeling and capacity calculation.

PubMed

Djordjevic, Ivan B

2012-12-10

Quantum mechanics has an important role in photosynthesis, magnetoreception, and evolution. There were many attempts in an effort to explain the structure of genetic code and transfer of information from DNA to protein by using the concepts of quantum mechanics. The existing biological quantum channel models are not sufficiently general to incorporate all relevant contributions responsible for imperfect protein synthesis. Moreover, the problem of determination of quantum biological channel capacity is still an open problem. To solve these problems, we construct the operator-sum representation of biological channel based on codon basekets (basis vectors), and determine the quantum channel model suitable for study of the quantum biological channel capacity and beyond. The transcription process, DNA point mutations, insertions, deletions, and translation are interpreted as the quantum noise processes. The various types of quantum errors are classified into several broad categories: (i) storage errors that occur in DNA itself as it represents an imperfect storage of genetic information, (ii) replication errors introduced during DNA replication process, (iii) transcription errors introduced during DNA to mRNA transcription, and (iv) translation errors introduced during the translation process. By using this model, we determine the biological quantum channel capacity and compare it against corresponding classical biological channel capacity. We demonstrate that the quantum biological channel capacity is higher than the classical one, for a coherent quantum channel model, suggesting that quantum effects have an important role in biological systems. The proposed model is of crucial importance towards future study of quantum DNA error correction, developing quantum mechanical model of aging, developing the quantum mechanical models for tumors/cancer, and study of intracellular dynamics in general.

[Impact of a software application to improve medication reconciliation at hospital discharge].

PubMed

Corral Baena, S; Garabito Sánchez, M J; Ruíz Rómero, M V; Vergara Díaz, M A; Martín Chacón, E R; Fernández Moyano, A

2014-01-01

To assess the impact of a software application to improve the quality of information concerning current patient medications and changes on the discharge report after hospitalization. To analyze the incidence of errors and to classify them. Quasi-experimental pre / post study with non-equivalent control group study. Medical patients at hospital discharge. implementation of a software application. Percentage of reconciled patient medication on discharge, and percentage of patients with more than one unjustified discrepancy. A total of 349 patients were assessed; 199 (pre-intervention phase) and 150 (post-intervention phase). Before the implementation of the application in 157 patients (78.8%) medication reconciliation had been completed; finding reconciliation errors in 99 (63.0%). The most frequent type of error, 339 (78.5%), was a missing dose or administration frequency information. After implementation, all the patient prescriptions were reconciled when the software was used. The percentage of patients with unjustified discrepancies decreased from 63.0% to 11.8% with the use of the application (p<.001). The main type of discrepancy found on using the application was confusing prescription, due to the fact that the professionals were not used to using the new tool. The use of a software application has been shown to improve the quality of the information on patient treatment on the hospital discharge report, but it is still necessary to continue development as a strategy for improving medication reconciliation. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

[Refractive errors among schoolchildren in the central region of Togo].

PubMed

Nonon Saa, K B; Atobian, K; Banla, M; Rédah, T; Maneh, N; Walser, A

2013-11-01

Untreated refractive errors represent the main visual impairment in the world but also the easiest to avoid. The goal of this survey is to use clinical and epidemiological data to efficiently plan distribution of corrective glasses in a project supported by the Swiss Red Cross in the central region of Togo. To achieve this goal, 66 primary schools were identified randomly in the catchment area of the project. The teachers at these schools were previously trained to test visual acuity (VA). The schoolchildren referred by these teachers were examined by eye care professionals. The schoolchildren with ametropia (VA≤7/10 in at least one eye) underwent cycloplegic autorefraction. Of a total of 19,252 registered schoolchildren, 13,039 underwent VA testing by the teachers (participation rate=68%). Among them, 366 cases of ametropia were identified (prevalence about 3%). The average age of the schoolchildren examined was 10.7±2.3years, with a sex ratio of 1.06. Autorefraction, which was performed for 37% of the schoolchildren with ametropia allowed them to be classified into three groups: hyperopia (4%), myopia (5%) and astigmatism of all types (91%). Regardless of the type of ametropia, the degree of severity was mild in 88%. The results of this survey have highlighted the importance of the teachers' contribution to eye care education in the struggle against refractive errors within the school environment, as well as helping to efficiently plan actions against ametropia. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Effect of bar-code technology on the safety of medication administration.

PubMed

Poon, Eric G; Keohane, Carol A; Yoon, Catherine S; Ditmore, Matthew; Bane, Anne; Levtzion-Korach, Osnat; Moniz, Thomas; Rothschild, Jeffrey M; Kachalia, Allen B; Hayes, Judy; Churchill, William W; Lipsitz, Stuart; Whittemore, Anthony D; Bates, David W; Gandhi, Tejal K

2010-05-06

Serious medication errors are common in hospitals and often occur during order transcription or administration of medication. To help prevent such errors, technology has been developed to verify medications by incorporating bar-code verification technology within an electronic medication-administration system (bar-code eMAR). We conducted a before-and-after, quasi-experimental study in an academic medical center that was implementing the bar-code eMAR. We assessed rates of errors in order transcription and medication administration on units before and after implementation of the bar-code eMAR. Errors that involved early or late administration of medications were classified as timing errors and all others as nontiming errors. Two clinicians reviewed the errors to determine their potential to harm patients and classified those that could be harmful as potential adverse drug events. We observed 14,041 medication administrations and reviewed 3082 order transcriptions. Observers noted 776 nontiming errors in medication administration on units that did not use the bar-code eMAR (an 11.5% error rate) versus 495 such errors on units that did use it (a 6.8% error rate)--a 41.4% relative reduction in errors (P<0.001). The rate of potential adverse drug events (other than those associated with timing errors) fell from 3.1% without the use of the bar-code eMAR to 1.6% with its use, representing a 50.8% relative reduction (P<0.001). The rate of timing errors in medication administration fell by 27.3% (P<0.001), but the rate of potential adverse drug events associated with timing errors did not change significantly. Transcription errors occurred at a rate of 6.1% on units that did not use the bar-code eMAR but were completely eliminated on units that did use it. Use of the bar-code eMAR substantially reduced the rate of errors in order transcription and in medication administration as well as potential adverse drug events, although it did not eliminate such errors. Our data show that the bar-code eMAR is an important intervention to improve medication safety. (ClinicalTrials.gov number, NCT00243373.) 2010 Massachusetts Medical Society

Decorrelation of the true and estimated classifier errors in high-dimensional settings.

PubMed

Hanczar, Blaise; Hua, Jianping; Dougherty, Edward R

2007-01-01

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, k-fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.

The quality of systematic reviews about interventions for refractive error can be improved: a review of systematic reviews.

PubMed

Mayo-Wilson, Evan; Ng, Sueko Matsumura; Chuck, Roy S; Li, Tianjing

2017-09-05

Systematic reviews should inform American Academy of Ophthalmology (AAO) Preferred Practice Pattern® (PPP) guidelines. The quality of systematic reviews related to the forthcoming Preferred Practice Pattern® guideline (PPP) Refractive Errors & Refractive Surgery is unknown. We sought to identify reliable systematic reviews to assist the AAO Refractive Errors & Refractive Surgery PPP. Systematic reviews were eligible if they evaluated the effectiveness or safety of interventions included in the 2012 PPP Refractive Errors & Refractive Surgery. To identify potentially eligible systematic reviews, we searched the Cochrane Eyes and Vision United States Satellite database of systematic reviews. Two authors identified eligible reviews and abstracted information about the characteristics and quality of the reviews independently using the Systematic Review Data Repository. We classified systematic reviews as "reliable" when they (1) defined criteria for the selection of studies, (2) conducted comprehensive literature searches for eligible studies, (3) assessed the methodological quality (risk of bias) of the included studies, (4) used appropriate methods for meta-analyses (which we assessed only when meta-analyses were reported), (5) presented conclusions that were supported by the evidence provided in the review. We identified 124 systematic reviews related to refractive error; 39 met our eligibility criteria, of which we classified 11 to be reliable. Systematic reviews classified as unreliable did not define the criteria for selecting studies (5; 13%), did not assess methodological rigor (10; 26%), did not conduct comprehensive searches (17; 44%), or used inappropriate quantitative methods (3; 8%). The 11 reliable reviews were published between 2002 and 2016. They included 0 to 23 studies (median = 9) and analyzed 0 to 4696 participants (median = 666). Seven reliable reviews (64%) assessed surgical interventions. Most systematic reviews of interventions for refractive error are low methodological quality. Following widely accepted guidance, such as Cochrane or Institute of Medicine standards for conducting systematic reviews, would contribute to improved patient care and inform future research.

On Identifying Clusters Within the C-type Asteroids of the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Poole, Renae; Ziffer, J.; Harvell, T.

2012-10-01

We applied AutoClass, a data mining technique based upon Bayesian Classification, to C-group asteroid colors in the Sloan Digital Sky Survey (SDSS). Previous taxonomic studies relied mostly on Principal Component Analysis (PCA) to differentiate asteroids within the C-group (e.g. B, G, F, Ch, Cg and Cb). AutoClass's advantage is that it calculates the most probable classification for us, removing the human factor from this part of the analysis. In our results, AutoClass divided the C-groups into two large classes and six smaller classes. The two large classes (n=4974 and 2033, respectively) display distinct regions with some overlap in color-vs-color plots. Each cluster's average spectrum is compared to 'typical' spectra of the C-group subtypes as defined by Tholen (1989) and each cluster's members are evaluated for consistency with previous taxonomies. Of the 117 asteroids classified as B-type in previous taxonomies, only 12 were found with SDSS colors that matched our criteria of having less than 0.1 magnitude error in u and 0.05 magnitude error in g, r, i, and z colors. Although this is a relatively small group, 11 of the 12 B-types were placed by AutoClass in the same cluster. By determining the C-group sub-classifications in the large SDSS database, this research furthers our understanding of the stratigraphy and composition of the main-belt.

Advanced adaptive computational methods for Navier-Stokes simulations in rotorcraft aerodynamics

NASA Technical Reports Server (NTRS)

Stowers, S. T.; Bass, J. M.; Oden, J. T.

1993-01-01

A phase 2 research and development effort was conducted in area transonic, compressible, inviscid flows with an ultimate goal of numerically modeling complex flows inherent in advanced helicopter blade designs. The algorithms and methodologies therefore are classified as adaptive methods, which are error estimation techniques for approximating the local numerical error, and automatically refine or unrefine the mesh so as to deliver a given level of accuracy. The result is a scheme which attempts to produce the best possible results with the least number of grid points, degrees of freedom, and operations. These types of schemes automatically locate and resolve shocks, shear layers, and other flow details to an accuracy level specified by the user of the code. The phase 1 work involved a feasibility study of h-adaptive methods for steady viscous flows, with emphasis on accurate simulation of vortex initiation, migration, and interaction. Phase 2 effort focused on extending these algorithms and methodologies to a three-dimensional topology.

A procedure used for a ground truth study of a land use map of North Alabama generated from LANDSAT data

NASA Technical Reports Server (NTRS)

Downs, S. W., Jr.; Sharma, G. C.; Bagwell, C.

1977-01-01

A land use map of a five county area in North Alabama was generated from LANDSAT data using a supervised classification algorithm. There was good overall agreement between the land use designated and known conditions, but there were also obvious discrepancies. In ground checking the map, two types of errors were encountered - shift and misclassification - and a method was developed to eliminate or greatly reduce the errors. Randomly selected study areas containing 2,525 pixels were analyzed. Overall, 76.3 percent of the pixels were correctly classified. A contingency coefficient of correlation was calculated to be 0.7 which is significant at the alpha = 0.01 level. The land use maps generated by computers from LANDSAT data are useful for overall land use by regional agencies. However, care must be used when making detailed analysis of small areas. The procedure used for conducting the ground truth study together with data from representative study areas is presented.

A comparison of image restoration approaches applied to three-dimensional confocal and wide-field fluorescence microscopy.

PubMed

Verveer, P. J; Gemkow, M. J; Jovin, T. M

1999-01-01

We have compared different image restoration approaches for fluorescence microscopy. The most widely used algorithms were classified with a Bayesian theory according to the assumed noise model and the type of regularization imposed. We considered both Gaussian and Poisson models for the noise in combination with Tikhonov regularization, entropy regularization, Good's roughness and without regularization (maximum likelihood estimation). Simulations of fluorescence confocal imaging were used to examine the different noise models and regularization approaches using the mean squared error criterion. The assumption of a Gaussian noise model yielded only slightly higher errors than the Poisson model. Good's roughness was the best choice for the regularization. Furthermore, we compared simulated confocal and wide-field data. In general, restored confocal data are superior to restored wide-field data, but given sufficient higher signal level for the wide-field data the restoration result may rival confocal data in quality. Finally, a visual comparison of experimental confocal and wide-field data is presented.

Placebo non-response measure in sequential parallel comparison design studies.

PubMed

Rybin, Denis; Doros, Gheorghe; Pencina, Michael J; Fava, Maurizio

2015-07-10

The Sequential Parallel Comparison Design (SPCD) is one of the novel approaches addressing placebo response. The analysis of SPCD data typically classifies subjects as 'placebo responders' or 'placebo non-responders'. Most current methods employed for analysis of SPCD data utilize only a part of the data collected during the trial. A repeated measures model was proposed for analysis of continuous outcomes that permitted the inclusion of information from all subjects into the treatment effect estimation. We describe here a new approach using a weighted repeated measures model that further improves the utilization of data collected during the trial, allowing the incorporation of information that is relevant to the placebo response, and dealing with the problem of possible misclassification of subjects. Our simulations show that when compared to the unweighted repeated measures model method, our approach performs as well or, under certain conditions, better, in preserving the type I error, achieving adequate power and minimizing the mean squared error. Copyright © 2015 John Wiley & Sons, Ltd.

Medication errors: definitions and classification

PubMed Central

Aronson, Jeffrey K

2009-01-01

To understand medication errors and to identify preventive strategies, we need to classify them and define the terms that describe them. The four main approaches to defining technical terms consider etymology, usage, previous definitions, and the Ramsey–Lewis method (based on an understanding of theory and practice). A medication error is ‘a failure in the treatment process that leads to, or has the potential to lead to, harm to the patient’. Prescribing faults, a subset of medication errors, should be distinguished from prescription errors. A prescribing fault is ‘a failure in the prescribing [decision-making] process that leads to, or has the potential to lead to, harm to the patient’. The converse of this, ‘balanced prescribing’ is ‘the use of a medicine that is appropriate to the patient's condition and, within the limits created by the uncertainty that attends therapeutic decisions, in a dosage regimen that optimizes the balance of benefit to harm’. This excludes all forms of prescribing faults, such as irrational, inappropriate, and ineffective prescribing, underprescribing and overprescribing. A prescription error is ‘a failure in the prescription writing process that results in a wrong instruction about one or more of the normal features of a prescription’. The ‘normal features’ include the identity of the recipient, the identity of the drug, the formulation, dose, route, timing, frequency, and duration of administration. Medication errors can be classified, invoking psychological theory, as knowledge-based mistakes, rule-based mistakes, action-based slips, and memory-based lapses. This classification informs preventive strategies. PMID:19594526

Classification based upon gene expression data: bias and precision of error rates.

PubMed

Wood, Ian A; Visscher, Peter M; Mengersen, Kerrie L

2007-06-01

Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

Toward a Taxonomy of Errors in Iranian EFL Learners' Basic-Level Writing

ERIC Educational Resources Information Center

Salmani Nodoushan, Mohammad Ali

2018-01-01

This study attempted at classifying common errors found in the written performance of lower- and upper-intermediate Iranian EFL learners. It engaged a rich corpus of EFL writing samples collected over a course of 20 years (between 1992 and 2011) from lower- and upper-intermediate EFL learners studying at various Iranian universities to provide a…

Automatic Detection of Preposition Errors in Learner Writing

ERIC Educational Resources Information Center

De Felice, Rachele; Pulman, Stephen

2009-01-01

In this article, we present an approach to the automatic correction of preposition errors in L2 English. Our system, based on a maximum entropy classifier, achieves average precision of 42% and recall of 35% on this task. The discussion of results obtained on correct and incorrect data aims to establish what characteristics of L2 writing prove…

Using Statistical Techniques and Web Search to Correct ESL Errors

ERIC Educational Resources Information Center

Gamon, Michael; Leacock, Claudia; Brockett, Chris; Dolan, William B.; Gao, Jianfeng; Belenko, Dmitriy; Klementiev, Alexandre

2009-01-01

In this paper we present a system for automatic correction of errors made by learners of English. The system has two novel aspects. First, machine-learned classifiers trained on large amounts of native data and a very large language model are combined to optimize the precision of suggested corrections. Second, the user can access real-life web…

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

Bayes classification of interferometric TOPSAR data

NASA Technical Reports Server (NTRS)

Michel, T. R.; Rodriguez, E.; Houshmand, B.; Carande, R.

1995-01-01

We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.

A survey of mindset theories of intelligence and medical error self-reporting among pediatric housestaff and faculty.

PubMed

Jegathesan, Mithila; Vitberg, Yaffa M; Pusic, Martin V

2016-02-11

Intelligence theory research has illustrated that people hold either "fixed" (intelligence is immutable) or "growth" (intelligence can be improved) mindsets and that these views may affect how people learn throughout their lifetime. Little is known about the mindsets of physicians, and how mindset may affect their lifetime learning and integration of feedback. Our objective was to determine if pediatric physicians are of the "fixed" or "growth" mindset and whether individual mindset affects perception of medical error reporting.  We sent an anonymous electronic survey to pediatric residents and attending pediatricians at a tertiary care pediatric hospital. Respondents completed the "Theories of Intelligence Inventory" which classifies individuals on a 6-point scale ranging from 1 (Fixed Mindset) to 6 (Growth Mindset). Subsequent questions collected data on respondents' recall of medical errors by self or others. We received 176/349 responses (50 %). Participants were equally distributed between mindsets with 84 (49 %) classified as "fixed" and 86 (51 %) as "growth". Residents, fellows and attendings did not differ in terms of mindset. Mindset did not correlate with the small number of reported medical errors. There is no dominant theory of intelligence (mindset) amongst pediatric physicians. The distribution is similar to that seen in the general population. Mindset did not correlate with error reports.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

PubMed

Mustaqeem, Anam; Anwar, Syed Muhammad; Majid, Muahammad

2018-01-01

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.

Performance Analysis of Classification Methods for Indoor Localization in Vlc Networks

NASA Astrophysics Data System (ADS)

Sánchez-Rodríguez, D.; Alonso-González, I.; Sánchez-Medina, J.; Ley-Bosch, C.; Díaz-Vilariño, L.

2017-09-01

Indoor localization has gained considerable attention over the past decade because of the emergence of numerous location-aware services. Research works have been proposed on solving this problem by using wireless networks. Nevertheless, there is still much room for improvement in the quality of the proposed classification models. In the last years, the emergence of Visible Light Communication (VLC) brings a brand new approach to high quality indoor positioning. Among its advantages, this new technology is immune to electromagnetic interference and has the advantage of having a smaller variance of received signal power compared to RF based technologies. In this paper, a performance analysis of seventeen machine leaning classifiers for indoor localization in VLC networks is carried out. The analysis is accomplished in terms of accuracy, average distance error, computational cost, training size, precision and recall measurements. Results show that most of classifiers harvest an accuracy above 90 %. The best tested classifier yielded a 99.0 % accuracy, with an average error distance of 0.3 centimetres.

Error-associated behaviors and error rates for robotic geology

NASA Technical Reports Server (NTRS)

Anderson, Robert C.; Thomas, Geb; Wagner, Jacob; Glasgow, Justin

2004-01-01

This study explores human error as a function of the decision-making process. One of many models for human decision-making is Rasmussen's decision ladder [9]. The decision ladder identifies the multiple tasks and states of knowledge involved in decision-making. The tasks and states of knowledge can be classified by the level of cognitive effort required to make the decision, leading to the skill, rule, and knowledge taxonomy (Rasmussen, 1987). Skill based decisions require the least cognitive effort and knowledge based decisions require the greatest cognitive effort. Errors can occur at any of the cognitive levels.

Neuroradiologic correlation with aphasias. Cortico-subcortical map of language.

PubMed

Jiménez de la Peña, M M; Gómez Vicente, L; García Cobos, R; Martínez de Vega, V

Aphasia is an acquired language disorder due to a cerebral lesion; it is characterized by errors in production, denomination, or comprehension of language. Although most aphasias are mixed, from a practical point of view they are classified into different types according to their main clinical features: Broca's aphasia, Wernicke's aphasia, conduction aphasia, transcortical aphasia, and alexia with or without agraphia. We present the clinical findings for the main subtypes of aphasia, illustrating them with imaging cases, and we provide an up-to-date review of the language network with images from functional magnetic resonance imaging and tractography. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Updating expected action outcome in the medial frontal cortex involves an evaluation of error type.

PubMed

Maier, Martin E; Steinhauser, Marco

2013-10-02

Forming expectations about the outcome of an action is an important prerequisite for action control and reinforcement learning in the human brain. The medial frontal cortex (MFC) has been shown to play an important role in the representation of outcome expectations, particularly when an update of expected outcome becomes necessary because an error is detected. However, error detection alone is not always sufficient to compute expected outcome because errors can occur in various ways and different types of errors may be associated with different outcomes. In the present study, we therefore investigate whether updating expected outcome in the human MFC is based on an evaluation of error type. Our approach was to consider an electrophysiological correlate of MFC activity on errors, the error-related negativity (Ne/ERN), in a task in which two types of errors could occur. Because the two error types were associated with different amounts of monetary loss, updating expected outcomes on error trials required an evaluation of error type. Our data revealed a pattern of Ne/ERN amplitudes that closely mirrored the amount of monetary loss associated with each error type, suggesting that outcome expectations are updated based on an evaluation of error type. We propose that this is achieved by a proactive evaluation process that anticipates error types by continuously monitoring error sources or by dynamically representing possible response-outcome relations.

Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

NASA Technical Reports Server (NTRS)

Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

1999-01-01

In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

Quantitative evaluation of phonetograms in the case of functional dysphonia.

PubMed

Airainer, R; Klingholz, F

1993-06-01

According to the laryngeal clinical findings, figures making up a scale were assigned to vocally trained and vocally untrained persons suffering from different types of functional dysphonia. The different types of dysphonia--from the manifested hypofunctional to the extreme hyperfunctional dysphonia--were classified by means of this scale. Besides, the subjects' phonetograms were measured and approximated by three ellipses, what rendered possible the definition of phonetogram parameters. The combining of selected phonetogram parameters to linear combinations served the purpose of a phonetographic evaluation. The linear combinations were to bring phonetographic and clinical evaluations into correspondence as accurately as possible. It was necessary to use different kinds of linear combinations for male and female singers and nonsingers. As a result of the reclassification of 71 and the new classification of 89 patients, it was possible to graduate the types of functional dysphonia by means of computer-aided phonetogram evaluation with a clinically acceptable error rate. This method proved to be an important supplement to the conventional diagnostics of functional dysphonia.

Clarification of terminology in medication errors: definitions and classification.

PubMed

Ferner, Robin E; Aronson, Jeffrey K

2006-01-01

We have previously described and analysed some terms that are used in drug safety and have proposed definitions. Here we discuss and define terms that are used in the field of medication errors, particularly terms that are sometimes misunderstood or misused. We also discuss the classification of medication errors. A medication error is a failure in the treatment process that leads to, or has the potential to lead to, harm to the patient. Errors can be classified according to whether they are mistakes, slips, or lapses. Mistakes are errors in the planning of an action. They can be knowledge based or rule based. Slips and lapses are errors in carrying out an action - a slip through an erroneous performance and a lapse through an erroneous memory. Classification of medication errors is important because the probabilities of errors of different classes are different, as are the potential remedies.

On the limits of Kagan's impulsive reflective distinction.

PubMed

Jones, B; McIntyre, L

1976-05-01

A logical analysis is made of the Matching Familiar Figures (MFF) Test on the basis of which children have been classified as "impulsive" or "reflective." The reflective strategy is implicitly preferred to the impulsive because the reflective child makes fewer errors though generally taking longer to make his first response. We show that the test allows the choice of a number of "game plans" and speed-accuracy tradeoffs which in practice may not be very different. Error rates may not indicate perceptual sensitivity, in any case, since sensitivity and response factors may be confounded in the error rate. Using a visual running-memory-span task to avoid the inherent difficulties of the MFF test, we found that children previously classified on the basis of that test as impulsive or reflective did not differ in recognition accuracy but did differ in response bias and response latency. Accuracy and bias are estimated by way of Luce's choice theory (Luce, 1963), and the results are discussed in those terms.

Tuning support vector machines for minimax and Neyman-Pearson classification.

PubMed

Davenport, Mark A; Baraniuk, Richard G; Scott, Clayton D

2010-10-01

This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost-sensitive SVM, here called the 2C-SVM, is equivalent to another formulation called the 2nu-SVM. We then exploit a characterization of the 2nu-SVM parameter space to develop a simple yet powerful approach to error estimation based on smoothing. In an extensive experimental study, we demonstrate that smoothing significantly improves the accuracy of cross-validation error estimates, leading to dramatic performance gains. Furthermore, we propose coordinate descent strategies that offer significant gains in computational efficiency, with little to no loss in performance.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Artificial neural network classifier predicts neuroblastoma patients' outcome.

PubMed

Cangelosi, Davide; Pelassa, Simone; Morini, Martina; Conte, Massimo; Bosco, Maria Carla; Eva, Alessandra; Sementa, Angela Rita; Varesio, Luigi

2016-11-08

More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis. We developed a robust classifier predicting neuroblastoma patients' outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.

Rapid crop cover mapping for the conterminous United States

USGS Publications Warehouse

Dahal, Devendra; Wylie, Bruce K.; Howard, Daniel

2018-01-01

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

46 CFR 108.187 - Ventilation for brush type electric motors in classified spaces.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Ventilation for brush type electric motors in classified... Ventilation for brush type electric motors in classified spaces. Ventilation for brush type electric motors in... Electrical Equipment in Hazardous Locations”, except audible and visual alarms may be used if shutting down...

Language abnormality in deaf people with schizophrenia: a problem with classifiers.

PubMed

Chatzidamianos, G; McCarthy, R A; Du Feu, M; Rosselló, J; McKenna, P J

2018-06-05

Although there is evidence for language abnormality in schizophrenia, few studies have examined sign language in deaf patients with the disorder. This is of potential interest because a hallmark of sign languages is their use of classifiers (semantic or entity classifiers), a reference-tracking device with few if any parallels in spoken languages. This study aimed to examine classifier production and comprehension in deaf signing adults with schizophrenia. Fourteen profoundly deaf signing adults with schizophrenia and 35 age- and IQ-matched deaf healthy controls completed a battery of tests assessing classifier and noun comprehension and production. The patients showed poorer performance than the healthy controls on comprehension and production of both nouns and entity classifiers, with the deficit being most marked in the production of classifiers. Classifier production errors affected handshape rather than other parameters such as movement and location. The findings suggest that schizophrenia affects language production in deaf patients with schizophrenia in a unique way not seen in hearing patients.

Accuracy of references and quotations in veterinary journals.

PubMed

Hinchcliff, K W; Bruce, N J; Powers, J D; Kipp, M L

1993-02-01

The accuracy of references and quotations used to substantiate statements of fact in articles published in 6 frequently cited veterinary journals was examined. Three hundred references were randomly selected, and the accuracy of each citation was examined. A subset of 100 references was examined for quotational accuracy; ie, the accuracy with which authors represented the work or assertions of the author being cited. Of the 300 references selected, 295 were located, and 125 major errors were found in 88 (29.8%) of them. Sixty-seven (53.6%) major errors were found involving authors, 12 (9.6%) involved the article title, 14 (11.2%) involved the book or journal title, and 32 (25.6%) involved the volume number, date, or page numbers. Sixty-eight minor errors were detected. The accuracy of 111 quotations from 95 citations in 65 articles was examined. Nine quotations were technical and not classified, 86 (84.3%) were classified as correct, 2 (1.9%) contained minor misquotations, and 14 (13.7%) contained major misquotations. We concluded that misquotations and errors in citations occur frequently in veterinary journals, but at a rate similar to that reported for other biomedical journals.

Daily Orthogonal Kilovoltage Imaging Using a Gantry-Mounted On-Board Imaging System Results in a Reduction in Radiation Therapy Delivery Errors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Russo, Gregory A., E-mail: gregory.russo@bmc.org; Qureshi, Muhammad M.; Truong, Minh-Tam

2012-11-01

Purpose: To determine whether the use of routine image guided radiation therapy (IGRT) using pretreatment on-board imaging (OBI) with orthogonal kilovoltage X-rays reduces treatment delivery errors. Methods and Materials: A retrospective review of documented treatment delivery errors from 2003 to 2009 was performed. Following implementation of IGRT in 2007, patients received daily OBI with orthogonal kV X-rays prior to treatment. The frequency of errors in the pre- and post-IGRT time frames was compared. Treatment errors (TEs) were classified as IGRT-preventable or non-IGRT-preventable. Results: A total of 71,260 treatment fractions were delivered to 2764 patients. A total of 135 (0.19%) TEsmore » occurred in 39 (1.4%) patients (3.2% in 2003, 1.1% in 2004, 2.5% in 2005, 2% in 2006, 0.86% in 2007, 0.24% in 2008, and 0.22% in 2009). In 2007, the TE rate decreased by >50% and has remained low (P = .00007, compared to before 2007). Errors were classified as being potentially preventable with IGRT (e.g., incorrect site, patient, or isocenter) vs. not. No patients had any IGRT-preventable TEs from 2007 to 2009, whereas there were 9 from 2003 to 2006 (1 in 2003, 2 in 2004, 2 in 2005, and 4 in 2006; P = .0058) before the implementation of IGRT. Conclusions: IGRT implementation has a patient safety benefit with a significant reduction in treatment delivery errors. As such, we recommend the use of IGRT in routine practice to complement existing quality assurance measures.« less

Daily orthogonal kilovoltage imaging using a gantry-mounted on-board imaging system results in a reduction in radiation therapy delivery errors.

PubMed

Russo, Gregory A; Qureshi, Muhammad M; Truong, Minh-Tam; Hirsch, Ariel E; Orlina, Lawrence; Bohrs, Harry; Clancy, Pauline; Willins, John; Kachnic, Lisa A

2012-11-01

To determine whether the use of routine image guided radiation therapy (IGRT) using pretreatment on-board imaging (OBI) with orthogonal kilovoltage X-rays reduces treatment delivery errors. A retrospective review of documented treatment delivery errors from 2003 to 2009 was performed. Following implementation of IGRT in 2007, patients received daily OBI with orthogonal kV X-rays prior to treatment. The frequency of errors in the pre- and post-IGRT time frames was compared. Treatment errors (TEs) were classified as IGRT-preventable or non-IGRT-preventable. A total of 71,260 treatment fractions were delivered to 2764 patients. A total of 135 (0.19%) TEs occurred in 39 (1.4%) patients (3.2% in 2003, 1.1% in 2004, 2.5% in 2005, 2% in 2006, 0.86% in 2007, 0.24% in 2008, and 0.22% in 2009). In 2007, the TE rate decreased by >50% and has remained low (P = .00007, compared to before 2007). Errors were classified as being potentially preventable with IGRT (e.g., incorrect site, patient, or isocenter) vs. not. No patients had any IGRT-preventable TEs from 2007 to 2009, whereas there were 9 from 2003 to 2006 (1 in 2003, 2 in 2004, 2 in 2005, and 4 in 2006; P = .0058) before the implementation of IGRT. IGRT implementation has a patient safety benefit with a significant reduction in treatment delivery errors. As such, we recommend the use of IGRT in routine practice to complement existing quality assurance measures. Copyright © 2012 Elsevier Inc. All rights reserved.

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

PubMed Central

Porter, Teresita M.; Golding, G. Brian

2012-01-01

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys. PMID:22558215

Frequency of subgroups of the antigen “a” among volunteer donors

PubMed

Parra-Jaramillo, Katherine; Chiriboga-Ponce, Rosa F

2018-01-01

The presence of weak variants of blood type A represents a challenge in the practice of immunohematology for discrepancies in the time of the classification. It is common in blood banks to perform a forward and reverse typing for the purpose of confirming the blood type, but not all the people with a subgroup A2 have developed anti-A1 antibodies. We present a descriptive, observational and transversal study that establishes the proportion of subgroups of A antigen with the analysis of manual tube technique and monoclonal antibodies like anti-A, anti-A1 (Dolichus biflorus lectins extract) and anti-H. The analysis involved a total of 818 samples of voluntary blood donor, selected by random sampling, which were initially classified as 737 of Type A, and 81 as Type AB, with a confidence level of 95% (alpha error of 5% and 3% of precision). The present study evaluated the existence of the subgroups A1, A2, A1B, A2B, A intermediate and A intB. It is recommended the identification of subgroups in different types of blood in the laboratory and blood banks. Copyright: © 2018 SecretarÍa de Salud

Confidence Preserving Machine for Facial Action Unit Detection

PubMed Central

Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.; Xiong, Zhang

2016-01-01

Facial action unit (AU) detection from video has been a long-standing problem in automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the Confidence Preserving Machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an “easy-to-hard” strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the testing stage, the confident classifiers provide “virtual labels” for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific (PS) classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iCPM that iteratively augments training samples to train the confident classifiers, and kCPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous datasets GFT [15], BP4D [56], DISFA [42], and RU-FACS [3] illustrate the benefits of the proposed CPM models over baseline methods and state-of-the-art semisupervised learning and transfer learning methods. PMID:27479964

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

Why hard-nosed executives should care about management theory.

PubMed

Christensen, Clayton M; Raynor, Michael E

2003-09-01

Theory often gets a bum rap among managers because it's associated with the word "theoretical," which connotes "impractical." But it shouldn't. Because experience is solely about the past, solid theories are the only way managers can plan future actions with any degree of confidence. The key word here is "solid." Gravity is a solid theory. As such, it lets us predict that if we step off a cliff we will fall, without actually having to do so. But business literature is replete with theories that don't seem to work in practice or actually contradict each other. How can a manager tell a good business theory from a bad one? The first step is understanding how good theories are built. They develop in three stages: gathering data, organizing it into categories highlighting significant differences, then making generalizations explaining what causes what, under which circumstances. For instance, professor Ananth Raman and his colleagues collected data showing that bar code-scanning systems generated notoriously inaccurate inventory records. These observations led them to classify the types of errors the scanning systems produced and the types of shops in which those errors most often occurred. Recently, some of Raman's doctoral students have worked as clerks to see exactly what kinds of behavior cause the errors. From this foundation, a solid theory predicting under which circumstances bar code systems work, and don't work, is beginning to emerge. Once we forgo one-size-fits-all explanations and insist that a theory describes the circumstances under which it does and doesn't work, we can bring predictable success to the world of management.

Land cover mapping of Greater Mesoamerica using MODIS data

USGS Publications Warehouse

Giri, Chandra; Jenkins, Clinton N.

2005-01-01

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

PubMed

Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

2015-08-01

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Development of a methodology for classifying software errors

NASA Technical Reports Server (NTRS)

Gerhart, S. L.

1976-01-01

A mathematical formalization of the intuition behind classification of software errors is devised and then extended to a classification discipline: Every classification scheme should have an easily discernible mathematical structure and certain properties of the scheme should be decidable (although whether or not these properties hold is relative to the intended use of the scheme). Classification of errors then becomes an iterative process of generalization from actual errors to terms defining the errors together with adjustment of definitions according to the classification discipline. Alternatively, whenever possible, small scale models may be built to give more substance to the definitions. The classification discipline and the difficulties of definition are illustrated by examples of classification schemes from the literature and a new study of observed errors in published papers of programming methodologies.

A theory of human error

NASA Technical Reports Server (NTRS)

Mcruer, D. T.; Clement, W. F.; Allen, R. W.

1981-01-01

Human errors tend to be treated in terms of clinical and anecdotal descriptions, from which remedial measures are difficult to derive. Correction of the sources of human error requires an attempt to reconstruct underlying and contributing causes of error from the circumstantial causes cited in official investigative reports. A comprehensive analytical theory of the cause-effect relationships governing propagation of human error is indispensable to a reconstruction of the underlying and contributing causes. A validated analytical theory of the input-output behavior of human operators involving manual control, communication, supervisory, and monitoring tasks which are relevant to aviation, maritime, automotive, and process control operations is highlighted. This theory of behavior, both appropriate and inappropriate, provides an insightful basis for investigating, classifying, and quantifying the needed cause-effect relationships governing propagation of human error.

An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

VarBin, a novel method for classifying true and false positive variants in NGS data

PubMed Central

2013-01-01

Background Variant discovery for rare genetic diseases using Illumina genome or exome sequencing involves screening of up to millions of variants to find only the one or few causative variant(s). Sequencing or alignment errors create "false positive" variants, which are often retained in the variant screening process. Methods to remove false positive variants often retain many false positive variants. This report presents VarBin, a method to prioritize variants based on a false positive variant likelihood prediction. Methods VarBin uses the Genome Analysis Toolkit variant calling software to calculate the variant-to-wild type genotype likelihood ratio at each variant change and position divided by read depth. The resulting Phred-scaled, likelihood-ratio by depth (PLRD) was used to segregate variants into 4 Bins with Bin 1 variants most likely true and Bin 4 most likely false positive. PLRD values were calculated for a proband of interest and 41 additional Illumina HiSeq, exome and whole genome samples (proband's family or unrelated samples). At variant sites without apparent sequencing or alignment error, wild type/non-variant calls cluster near -3 PLRD and variant calls typically cluster above 10 PLRD. Sites with systematic variant calling problems (evident by variant quality scores and biases as well as displayed on the iGV viewer) tend to have higher and more variable wild type/non-variant PLRD values. Depending on the separation of a proband's variant PLRD value from the cluster of wild type/non-variant PLRD values for background samples at the same variant change and position, the VarBin method's classification is assigned to each proband variant (Bin 1 to Bin 4). Results To assess VarBin performance, Sanger sequencing was performed on 98 variants in the proband and background samples. True variants were confirmed in 97% of Bin 1 variants, 30% of Bin 2, and 0% of Bin 3/Bin 4. Conclusions These data indicate that VarBin correctly classifies the majority of true variants as Bin 1 and Bin 3/4 contained only false positive variants. The "uncertain" Bin 2 contained both true and false positive variants. Future work will further differentiate the variants in Bin 2. PMID:24266885

Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

NASA Astrophysics Data System (ADS)

Young, A. J.; Kuiken, T. A.; Hargrove, L. J.

2014-10-01

Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition-based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.

Creation of a Digital Surface Model and Extraction of Coarse Woody Debris from Terrestrial Laser Scans in an Open Eucalypt Woodland

NASA Astrophysics Data System (ADS)

Muir, J.; Phinn, S. R.; Armston, J.; Scarth, P.; Eyre, T.

2014-12-01

Coarse woody debris (CWD) provides important habitat for many species and plays a vital role in nutrient cycling within an ecosystem. In addition, CWD makes an important contribution to forest biomass and fuel loads. Airborne or space based remote sensing instruments typically do not detect CWD beneath the forest canopy. Terrestrial laser scanning (TLS) provides a ground based method for three-dimensional (3-D) reconstruction of surface features and CWD. This research produced a 3-D reconstruction of the ground surface and automatically classified coarse woody debris from registered TLS scans. The outputs will be used to inform the development of a site-based index for the assessment of forest condition, and quantitative assessments of biomass and fuel loads. A survey grade terrestrial laser scanner (Riegl VZ400) was used to scan 13 positions, in an open eucalypt woodland site at Karawatha Forest Park, near Brisbane, Australia. Scans were registered, and a digital surface model (DSM) produced using an intensity threshold and an iterative morphological filter. The DSMs produced from single scans were compared to the registered multi-scan point cloud using standard error metrics including: Root Mean Squared Error (RMSE), Mean Squared Error (MSE), range, absolute error and signed error. In addition the DSM was compared to a Digital Elevation Model (DEM) produced from Airborne Laser Scanning (ALS). Coarse woody debris was subsequently classified from the DSM using laser pulse properties, including: width and amplitude, as well as point spatial relationships (e.g. nearest neighbour slope vectors). Validation of the coarse woody debris classification was completed using true-colour photographs co-registered to the TLS point cloud. The volume and length of the coarse woody debris was calculated from the classified point cloud. A representative network of TLS sites will allow for up-scaling to large area assessment using airborne or space based sensors to monitor forest condition, biomass and fuel loads.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

On the use of interaction error potentials for adaptive brain computer interfaces.

PubMed

Llera, A; van Gerven, M A J; Gómez, V; Jensen, O; Kappen, H J

2011-12-01

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Learning with imperfectly labeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Artificial neural networks to predict activity type and energy expenditure in youth.

PubMed

Trost, Stewart G; Wong, Weng-Keen; Pfeiffer, Karen A; Zheng, Yonglei

2012-09-01

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous-intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip, and VO2 was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wootton, L; Nyflot, M; Ford, E

2016-06-15

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributedmore » (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for Healthcare Research and Quality, grant number R18 HS022244-01.« less

Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.

2014-01-01

The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic usedmore » fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this work are artificially low, because the models were trained, optimized, and tested on simulated, noise-free data. However, these results indicate that the developed models may generalize well to new data and that the proposed approach constitutes a viable first step in developing a fuel characterization algorithm based on gamma spectra.« less

Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.

A novel scheme for abnormal cell detection in Pap smear images

NASA Astrophysics Data System (ADS)

Zhao, Tong; Wachman, Elliot S.; Farkas, Daniel L.

2004-07-01

Finding malignant cells in Pap smear images is a "needle in a haystack"-type problem, tedious, labor-intensive and error-prone. It is therefore desirable to have an automatic screening tool in order that human experts can concentrate on the evaluation of the more difficult cases. Most research on automatic cervical screening tries to extract morphometric and texture features at the cell level, in accordance with the NIH "The Bethesda System" rules. Due to variances in image quality and features, such as brightness, magnification and focus, morphometric and texture analysis is insufficient to provide robust cervical cancer detection. Using a microscopic spectral imaging system, we have produced a set of multispectral Pap smear images with wavelengths from 400 nm to 690 nm, containing both spectral signatures and spatial attributes. We describe a novel scheme that combines spatial information (including texture and morphometric features) with spectral information to significantly improve abnormal cell detection. Three kinds of wavelet features, orthogonal, bi-orthogonal and non-orthogonal, are carefully chosen to optimize recognition performance. Multispectral feature sets are then extracted in the wavelet domain. Using a Back-Propagation Neural Network classifier that greatly decreases the influence of spurious events, we obtain a classification error rate of 5%. Cell morphometric features, such as area and shape, are then used to eliminate most remaining small artifacts. We report initial results from 149 cells from 40 separate image sets, in which only one abnormal cell was missed (TPR = 97.6%) and one normal cell was falsely classified as cancerous (FPR = 1%).

Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment.

PubMed

Sirgo, Gonzalo; Esteban, Federico; Gómez, Josep; Moreno, Gerard; Rodríguez, Alejandro; Blanch, Lluis; Guardiola, Juan José; Gracia, Rafael; De Haro, Lluis; Bodí, María

2018-04-01

Big data analytics promise insights into healthcare processes and management, improving outcomes while reducing costs. However, data quality is a major challenge for reliable results. Business process discovery techniques and an associated data model were used to develop data management tool, ICU-DaMa, for extracting variables essential for overseeing the quality of care in the intensive care unit (ICU). To determine the feasibility of using ICU-DaMa to automatically extract variables for the minimum dataset and ICU quality indicators from the clinical information system (CIS). The Wilcoxon signed-rank test and Fisher's exact test were used to compare the values extracted from the CIS with ICU-DaMa for 25 variables from all patients attended in a polyvalent ICU during a two-month period against the gold standard of values manually extracted by two trained physicians. Discrepancies with the gold standard were classified into plausibility, conformance, and completeness errors. Data from 149 patients were included. Although there were no significant differences between the automatic method and the manual method, we detected differences in values for five variables, including one plausibility error and two conformance and completeness errors. Plausibility: 1) Sex, ICU-DaMa incorrectly classified one male patient as female (error generated by the Hospital's Admissions Department). Conformance: 2) Reason for isolation, ICU-DaMa failed to detect a human error in which a professional misclassified a patient's isolation. 3) Brain death, ICU-DaMa failed to detect another human error in which a professional likely entered two mutually exclusive values related to the death of the patient (brain death and controlled donation after circulatory death). Completeness: 4) Destination at ICU discharge, ICU-DaMa incorrectly classified two patients due to a professional failing to fill out the patient discharge form when thepatients died. 5) Length of continuous renal replacement therapy, data were missing for one patient because the CRRT device was not connected to the CIS. Automatic generation of minimum dataset and ICU quality indicators using ICU-DaMa is feasible. The discrepancies were identified and can be corrected by improving CIS ergonomics, training healthcare professionals in the culture of the quality of information, and using tools for detecting and correcting data errors. Copyright © 2018 Elsevier B.V. All rights reserved.

Multicategory nets of single-layer perceptrons: complexity and sample-size issues.

PubMed

Raudys, Sarunas; Kybartas, Rimantas; Zavadskas, Edmundas Kazimieras

2010-05-01

The standard cost function of multicategory single-layer perceptrons (SLPs) does not minimize the classification error rate. In order to reduce classification error, it is necessary to: 1) refuse the traditional cost function, 2) obtain near to optimal pairwise linear classifiers by specially organized SLP training and optimal stopping, and 3) fuse their decisions properly. To obtain better classification in unbalanced training set situations, we introduce the unbalance correcting term. It was found that fusion based on the Kulback-Leibler (K-L) distance and the Wu-Lin-Weng (WLW) method result in approximately the same performance in situations where sample sizes are relatively small. The explanation for this observation is by theoretically known verity that an excessive minimization of inexact criteria becomes harmful at times. Comprehensive comparative investigations of six real-world pattern recognition (PR) problems demonstrated that employment of SLP-based pairwise classifiers is comparable and as often as not outperforming the linear support vector (SV) classifiers in moderate dimensional situations. The colored noise injection used to design pseudovalidation sets proves to be a powerful tool for facilitating finite sample problems in moderate-dimensional PR tasks.

Classification of resistance to passive motion using minimum probability of error criterion.

PubMed

Chan, H C; Manry, M T; Kondraske, G V

1987-01-01

Neurologists diagnose many muscular and nerve disorders by classifying the resistance to passive motion of patients' limbs. Over the past several years, a computer-based instrument has been developed for automated measurement and parameterization of this resistance. In the device, a voluntarily relaxed lower extremity is moved at constant velocity by a motorized driver. The torque exerted on the extremity by the machine is sampled, along with the angle of the extremity. In this paper a computerized technique is described for classifying a patient's condition as 'Normal' or 'Parkinson disease' (rigidity), from the torque versus angle curve for the knee joint. A Legendre polynomial, fit to the curve, is used to calculate a set of eight normally distributed features of the curve. The minimum probability of error approach is used to classify the curve as being from a normal or Parkinson disease patient. Data collected from 44 different subjects was processes and the results were compared with an independent physician's subjective assessment of rigidity. There is agreement in better than 95% of the cases, when all of the features are used.

A simulation study of the effects of land cover and crop type on sensing soil moisture with an orbital C-band radar

NASA Technical Reports Server (NTRS)

Dobson, M. C.; Ulaby, F. T.; Moezzi, S.; Roth, E.

1983-01-01

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1 km, and 3 km by 3 km, and each is processed using more than 23 independent samples. Moisture classification errors are examined as a function of land-cover distribution, field-size distribution, and local topographic relief for the full test site and also for subregions of cropland, urban areas, woodland, and pasture/rangeland. Results show that a radar resolution of 100 m by 100 m yields the most robust classification accuracies.

Review of calcium methodologies.

PubMed

Zak, B; Epstein, E; Baginski, E S

1975-01-01

A review of calcium methodologies for serum has been described. The analytical systems developed over the past century have been classified as to type beginning with gravimetry and extending to isotope dilution-mass spectrometry by covering all of the commonly used technics which have evolved during that period. Screening and referee procedures are discussed along with comparative sensitivities encountered between atomic absorption spectrophotometry and molecular absorption spectrophotometry. A procedure involving a simple direct reaction for serum calcium using cresolphthalein complexone is recommended in which high blanks are minimized by repressing the ionization of the color reagent on lowering the dielectric constant characteristics of the mixture with dimethylsulfoxide. Reaction characteristics, errors which can be encountered, normal ranges and an interpretative resume are included in its discussion.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Spelling in adolescents with dyslexia: errors and modes of assessment.

PubMed

Tops, Wim; Callens, Maaike; Bijn, Evi; Brysbaert, Marc

2014-01-01

In this study we focused on the spelling of high-functioning students with dyslexia. We made a detailed classification of the errors in a word and sentence dictation task made by 100 students with dyslexia and 100 matched control students. All participants were in the first year of their bachelor's studies and had Dutch as mother tongue. Three main error categories were distinguished: phonological, orthographic, and grammatical errors (on the basis of morphology and language-specific spelling rules). The results indicated that higher-education students with dyslexia made on average twice as many spelling errors as the controls, with effect sizes of d ≥ 2. When the errors were classified as phonological, orthographic, or grammatical, we found a slight dominance of phonological errors in students with dyslexia. Sentence dictation did not provide more information than word dictation in the correct classification of students with and without dyslexia. © Hammill Institute on Disabilities 2012.

Performance of digital RGB reflectance color extraction for plaque lesion

NASA Astrophysics Data System (ADS)

Hashim, Hadzli; Taib, Mohd Nasir; Jailani, Rozita; Sulaiman, Saadiah; Baba, Roshidah

2005-01-01

Several clinical psoriasis lesion groups are been studied for digital RGB color features extraction. Previous works have used samples size that included all the outliers lying beyond the standard deviation factors from the peak histograms. This paper described the statistical performances of the RGB model with and without removing these outliers. Plaque lesion is experimented with other types of psoriasis. The statistical tests are compared with respect to three samples size; the original 90 samples, the first size reduction by removing outliers from 2 standard deviation distances (2SD) and the second size reduction by removing outliers from 1 standard deviation distance (1SD). Quantification of data images through the normal/direct and differential of the conventional reflectance method is considered. Results performances are concluded by observing the error plots with 95% confidence interval and findings of the inference T-tests applied. The statistical tests outcomes have shown that B component for conventional differential method can be used to distinctively classify plaque from the other psoriasis groups in consistent with the error plots finding with an improvement in p-value greater than 0.5.

Introduction to cognitive processes of expert pilots.

PubMed

Adams, R J; Ericsson, A E

2000-10-01

This report addresses the historical problem that a very high percentage of accidents have been classified as involving "pilot error." Through extensive research since 1977, the Federal Aviation Administration determined that the predominant underlying cause of these types of accidents involved decisional problems or cognitive information processing. To attack these problems, Aeronautical Decision Making (ADM) training materials were developed and tested for ten years. Since the publication of the ADM training manuals in 1987, significant reductions in human performance error (HPE) accidents have been documented both in the U.S. and world wide. However, shortcomings have been observed in the use of these materials for recurrency training and in their relevance to more experienced pilots. The following discussion defines the differences between expert and novice decision makers from a cognitive information processing perspective, correlates the development of expert pilot cognitive processes with training and experience, and reviews accident scenarios which exemplify those processes. This introductory material is a necessary prerequisite to an understanding of how to formulate expert pilot decision making training innovations; and, to continue the record of improved safety through ADM training.

Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

PubMed Central

Manyakov, Nikolay V.; Chumerin, Nikolay; Combaz, Adrien; Van Hulle, Marc M.

2011-01-01

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects. PMID:21941530

Exploring diversity in ensemble classification: Applications in large area land cover mapping

NASA Astrophysics Data System (ADS)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.

Enhancing atlas based segmentation with multiclass linear classifiers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr

Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible localmore » registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

The Causes of Errors in Clinical Reasoning: Cognitive Biases, Knowledge Deficits, and Dual Process Thinking.

PubMed

Norman, Geoffrey R; Monteiro, Sandra D; Sherbino, Jonathan; Ilgen, Jonathan S; Schmidt, Henk G; Mamede, Silvia

2017-01-01

Contemporary theories of clinical reasoning espouse a dual processing model, which consists of a rapid, intuitive component (Type 1) and a slower, logical and analytical component (Type 2). Although the general consensus is that this dual processing model is a valid representation of clinical reasoning, the causes of diagnostic errors remain unclear. Cognitive theories about human memory propose that such errors may arise from both Type 1 and Type 2 reasoning. Errors in Type 1 reasoning may be a consequence of the associative nature of memory, which can lead to cognitive biases. However, the literature indicates that, with increasing expertise (and knowledge), the likelihood of errors decreases. Errors in Type 2 reasoning may result from the limited capacity of working memory, which constrains computational processes. In this article, the authors review the medical literature to answer two substantial questions that arise from this work: (1) To what extent do diagnostic errors originate in Type 1 (intuitive) processes versus in Type 2 (analytical) processes? (2) To what extent are errors a consequence of cognitive biases versus a consequence of knowledge deficits?The literature suggests that both Type 1 and Type 2 processes contribute to errors. Although it is possible to experimentally induce cognitive biases, particularly availability bias, the extent to which these biases actually contribute to diagnostic errors is not well established. Educational strategies directed at the recognition of biases are ineffective in reducing errors; conversely, strategies focused on the reorganization of knowledge to reduce errors have small but consistent benefits.

Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report

EIA Publications

2016-01-01

This document lists types of potential errors in EIA estimates published in the WNGSR. Survey errors are an unavoidable aspect of data collection. Error is inherent in all collected data, regardless of the source of the data and the care and competence of data collectors. The type and extent of error depends on the type and characteristics of the survey.

Virtual setting for training in interpreting mammography images

NASA Astrophysics Data System (ADS)

Pezzuol, J. L.; Abreu, F. D. L.; Silva, S. M.; Tendolini, A.; Bissaco, M. A. Se; Rodrigues, S. C. M.

2017-03-01

This work presents a web system for the training of students or residents (users) interested in the detection of breast density in mammography images. The system consists of a breast imaging database with breast density types classified and demarcated by the specialist (tutor) or online database. The planning was based on ISO / IEC 12207. Through the browser (desktop or notebook), the user will visualize the breast images and in them will realize the markings of the density region and even classify them per the BI-RADS protocol. After marking, this will be compared to the gold standard already existing in the image base, and then the system will inform if the area demarcation has been set or not. The shape of this marking is similar to the paint brush. The evaluation was based on ISO / IEC 1926 or 25010: 2011 by 3 software development specialists and 3 in mammary radiology, evaluating usability, configuration, performance and System interface through the Likert scale-based questionnaire. Where they have totally agreed on usability, configuration, performance and partially on the interface. And as a good thing: the system is able to be accessed anywhere and at any time, the hit or error response is in real time, it can be used in the educational area, the limit of the amount of images will depend on the size of the computer memory, At the end the system sends the results achieved by e-mail to the user, reproduction of the system on any type of screen, complementation of the system with other types of breast structures. Negative points are the need for internet.

Is neonatal neurological damage in the delivery room avoidable? Experience of 33 levels I and II maternity units of a French perinatal network.

PubMed

Dupuis, O; Dupont, C; Gaucherand, P; Rudigoz, R-C; Fernandez, M P; Peigne, E; Labaune, J M

2007-09-01

To determine the frequency of avoidable neonatal neurological damage. We carried out a retrospective study from January 1st to December 31st 2003, including all children transferred from a level I or II maternity unit for suspected neurological damage (SND). Only cases confirmed by a persistent abnormality on clinical examination, EEG, transfontanelle ultrasound scan, CT scan or cerebral MRI were retained. Each case was studied in detail by an expert committee and classified as "avoidable", "unavoidable" or "of indeterminate avoidability." The management of "avoidable" cases was analysed to identify potentially avoidable factors (PAFs): not taking into account a major risk factor (PAF1), diagnostic errors (PAF2), suboptimal decision to delivery interval (PAF3) and mechanical complications (PAF4). In total, 77 children were transferred for SND; two cases were excluded (inaccessible medical files). Forty of the 75 cases of SND included were confirmed: 29 were "avoidable", 8 were "unavoidable" and 3 were "of indeterminate avoidability". Analysis of the 29 avoidable cases identified 39 PAFs: 18 PAF1, 5 PAF2, 10 PAF3 and 6 PAF4. Five had no classifiable PAF (0 death), 11 children had one type of PAF (one death), 11 children had two types of PAF (3 deaths), 2 had three types of PAF (2 deaths). Three quarters of the confirmed cases of neurological damage occurring in levels I and II maternity units of the Aurore network in 2003 were avoidable. Five out of six cases resulting in early death involved several potentially avoidable factors.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

A neuroimaging study of conflict during word recognition.

PubMed

Riba, Jordi; Heldmann, Marcus; Carreiras, Manuel; Münte, Thomas F

2010-08-04

Using functional magnetic resonance imaging the neural activity associated with error commission and conflict monitoring in a lexical decision task was assessed. In a cohort of 20 native speakers of Spanish conflict was introduced by presenting words with high and low lexical frequency and pseudo-words with high and low syllabic frequency for the first syllable. Erroneous versus correct responses showed activation in the frontomedial and left inferior frontal cortex. A similar pattern was found for correctly classified words of low versus high lexical frequency and for correctly classified pseudo-words of high versus low syllabic frequency. Conflict-related activations for language materials largely overlapped with error-induced activations. The effect of syllabic frequency underscores the role of sublexical processing in visual word recognition and supports the view that the initial syllable mediates between the letter and word level.

Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

NASA Astrophysics Data System (ADS)

Sakuma, Jun; Wright, Rebecca N.

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.

PubMed

Khondoker, Mizanur R; Bachmann, Till T; Mewissen, Muriel; Dickinson, Paul; Dobrzelecki, Bartosz; Campbell, Colin J; Mount, Andrew R; Walton, Anthony J; Crain, Jason; Schulze, Holger; Giraud, Gerard; Ross, Alan J; Ciani, Ilenia; Ember, Stuart W J; Tlili, Chaker; Terry, Jonathan G; Grant, Eilidh; McDonnell, Nicola; Ghazal, Peter

2010-12-01

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).

Towards more reliable automated multi-dose dispensing: retrospective follow-up study on medication dose errors and product defects.

PubMed

Palttala, Iida; Heinämäki, Jyrki; Honkanen, Outi; Suominen, Risto; Antikainen, Osmo; Hirvonen, Jouni; Yliruusi, Jouko

2013-03-01

To date, little is known on applicability of different types of pharmaceutical dosage forms in an automated high-speed multi-dose dispensing process. The purpose of the present study was to identify and further investigate various process-induced and/or product-related limitations associated with multi-dose dispensing process. The rates of product defects and dose dispensing errors in automated multi-dose dispensing were retrospectively investigated during a 6-months follow-up period. The study was based on the analysis of process data of totally nine automated high-speed multi-dose dispensing systems. Special attention was paid to the dependence of multi-dose dispensing errors/product defects and pharmaceutical tablet properties (such as shape, dimensions, weight, scored lines, coatings, etc.) to profile the most suitable forms of tablets for automated dose dispensing systems. The relationship between the risk of errors in dose dispensing and tablet characteristics were visualized by creating a principal component analysis (PCA) model for the outcome of dispensed tablets. The two most common process-induced failures identified in the multi-dose dispensing are predisposal of tablet defects and unexpected product transitions in the medication cassette (dose dispensing error). The tablet defects are product-dependent failures, while the tablet transitions are dependent on automated multi-dose dispensing systems used. The occurrence of tablet defects is approximately twice as common as tablet transitions. Optimal tablet preparation for the high-speed multi-dose dispensing would be a round-shaped, relatively small/middle-sized, film-coated tablet without any scored line. Commercial tablet products can be profiled and classified based on their suitability to a high-speed multi-dose dispensing process.

Assessing data quality and the variability of source data verification auditing methods in clinical research settings.

PubMed

Houston, Lauren; Probst, Yasmine; Martin, Allison

2018-05-18

Data audits within clinical settings are extensively used as a major strategy to identify errors, monitor study operations and ensure high-quality data. However, clinical trial guidelines are non-specific in regards to recommended frequency, timing and nature of data audits. The absence of a well-defined data quality definition and method to measure error undermines the reliability of data quality assessment. This review aimed to assess the variability of source data verification (SDV) auditing methods to monitor data quality in a clinical research setting. The scientific databases MEDLINE, Scopus and Science Direct were searched for English language publications, with no date limits applied. Studies were considered if they included data from a clinical trial or clinical research setting and measured and/or reported data quality using a SDV auditing method. In total 15 publications were included. The nature and extent of SDV audit methods in the articles varied widely, depending upon the complexity of the source document, type of study, variables measured (primary or secondary), data audit proportion (3-100%) and collection frequency (6-24 months). Methods for coding, classifying and calculating error were also inconsistent. Transcription errors and inexperienced personnel were the main source of reported error. Repeated SDV audits using the same dataset demonstrated ∼40% improvement in data accuracy and completeness over time. No description was given in regards to what determines poor data quality in clinical trials. A wide range of SDV auditing methods are reported in the published literature though no uniform SDV auditing method could be determined for "best practice" in clinical trials. Published audit methodology articles are warranted for the development of a standardised SDV auditing method to monitor data quality in clinical research settings. Copyright © 2018. Published by Elsevier Inc.

Data mining: Potential applications in research on nutrition and health.

PubMed

Batterham, Marijka; Neale, Elizabeth; Martin, Allison; Tapsell, Linda

2017-02-01

Data mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention. Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables' importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01). Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research. © 2017 Dietitians Association of Australia.

Differences among Job Positions Related to Communication Errors at Construction Sites

NASA Astrophysics Data System (ADS)

Takahashi, Akiko; Ishida, Toshiro

In a previous study, we classified the communicatio n errors at construction sites as faulty intention and message pattern, inadequate channel pattern, and faulty comprehension pattern. This study seeks to evaluate the degree of risk of communication errors and to investigate differences among people in various job positions in perception of communication error risk . Questionnaires based on the previous study were a dministered to construction workers (n=811; 149 adminis trators, 208 foremen and 454 workers). Administrators evaluated all patterns of communication error risk equally. However, foremen and workers evaluated communication error risk differently in each pattern. The common contributing factors to all patterns wer e inadequate arrangements before work and inadequate confirmation. Some factors were common among patterns but other factors were particular to a specific pattern. To help prevent future accidents at construction sites, administrators should understand how people in various job positions perceive communication errors and propose human factors measures to prevent such errors.

Robust Combining of Disparate Classifiers Through Order Statistics

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2001-01-01

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

Categorizing accident sequences in the external radiotherapy for risk analysis

PubMed Central

2013-01-01

Purpose This study identifies accident sequences from the past accidents in order to help the risk analysis application to the external radiotherapy. Materials and Methods This study reviews 59 accidental cases in two retrospective safety analyses that have collected the incidents in the external radiotherapy extensively. Two accident analysis reports that accumulated past incidents are investigated to identify accident sequences including initiating events, failure of safety measures, and consequences. This study classifies the accidents by the treatments stages and sources of errors for initiating events, types of failures in the safety measures, and types of undesirable consequences and the number of affected patients. Then, the accident sequences are grouped into several categories on the basis of similarity of progression. As a result, these cases can be categorized into 14 groups of accident sequence. Results The result indicates that risk analysis needs to pay attention to not only the planning stage, but also the calibration stage that is committed prior to the main treatment process. It also shows that human error is the largest contributor to initiating events as well as to the failure of safety measures. This study also illustrates an event tree analysis for an accident sequence initiated in the calibration. Conclusion This study is expected to provide sights into the accident sequences for the prospective risk analysis through the review of experiences. PMID:23865005

Evaluation of a UMLS Auditing Process of Semantic Type Assignments

PubMed Central

Gu, Huanying; Hripcsak, George; Chen, Yan; Morrey, C. Paul; Elhanan, Gai; Cimino, James J.; Geller, James; Perl, Yehoshua

2007-01-01

The UMLS is a terminological system that integrates many source terminologies. Each concept in the UMLS is assigned one or more semantic types from the Semantic Network, an upper level ontology for biomedicine. Due to the complexity of the UMLS, errors exist in the semantic type assignments. Finding assignment errors may unearth modeling errors. Even with sophisticated tools, discovering assignment errors requires manual review. In this paper we describe the evaluation of an auditing project of UMLS semantic type assignments. We studied the performance of the auditors who reviewed potential errors. We found that four auditors, interacting according to a multi-step protocol, identified a high rate of errors (one or more errors in 81% of concepts studied) and that results were sufficiently reliable (0.67 to 0.70) for the two most common types of errors. However, reliability was low for each individual auditor, suggesting that review of potential errors is resource-intensive. PMID:18693845

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

2016-02-01

Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

USGS Publications Warehouse

Wright, C.; Gallant, Alisa L.

2007-01-01

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Supernova Cosmology Inference with Probabilistic Photometric Redshifts (SCIPPR)

NASA Astrophysics Data System (ADS)

Peters, Christina; Malz, Alex; Hlozek, Renée

2018-01-01

The Bayesian Estimation Applied to Multiple Species (BEAMS) framework employs probabilistic supernova type classifications to do photometric SN cosmology. This work extends BEAMS to replace high-confidence spectroscopic redshifts with photometric redshift probability density functions, a capability that will be essential in the era the Large Synoptic Survey Telescope and other next-generation photometric surveys where it will not be possible to perform spectroscopic follow up on every SN. We present the Supernova Cosmology Inference with Probabilistic Photometric Redshifts (SCIPPR) Bayesian hierarchical model for constraining the cosmological parameters from photometric lightcurves and host galaxy photometry, which includes selection effects and is extensible to uncertainty in the redshift-dependent supernova type proportions. We create a pair of realistic mock catalogs of joint posteriors over supernova type, redshift, and distance modulus informed by photometric supernova lightcurves and over redshift from simulated host galaxy photometry. We perform inference under our model to obtain a joint posterior probability distribution over the cosmological parameters and compare our results with other methods, namely: a spectroscopic subset, a subset of high probability photometrically classified supernovae, and reducing the photometric redshift probability to a single measurement and error bar.

Classification of mislabelled microarrays using robust sparse logistic regression.

PubMed

Bootkrajang, Jakramate; Kabán, Ata

2013-04-01

Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make reliable inferences from the data is compromised. Yet, few methods are currently available in the bioinformatics literature to deal with this problem. The few existing methods focus on data cleansing alone, without reference to classification, and their performance crucially depends on some tuning parameters. In this article, we develop a new method to detect mislabelled arrays simultaneously with learning a sparse logistic regression classifier. Our method may be seen as a label-noise robust extension of the well-known and successful Bayesian logistic regression classifier. To account for possible mislabelling, we formulate a label-flipping process as part of the classifier. The regularization parameter is automatically set using Bayesian regularization, which not only saves the computation time that cross-validation would take, but also eliminates any unwanted effects of label noise when setting the regularization parameter. Extensive experiments with both synthetic data and real microarray datasets demonstrate that our approach is able to counter the bad effects of labelling errors in terms of predictive performance, it is effective at identifying marker genes and simultaneously it detects mislabelled arrays to high accuracy. The code is available from http://cs.bham.ac.uk/∼jxb008. Supplementary data are available at Bioinformatics online.

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

NASA Astrophysics Data System (ADS)

Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

2017-10-01

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

Detecting medication errors in the New Zealand pharmacovigilance database: a retrospective analysis.

PubMed

Kunac, Desireé L; Tatley, Michael V

2011-01-01

Despite the traditional focus being adverse drug reactions (ADRs), pharmacovigilance centres have recently been identified as a potentially rich and important source of medication error data. To identify medication errors in the New Zealand Pharmacovigilance database (Centre for Adverse Reactions Monitoring [CARM]), and to describe the frequency and characteristics of these events. A retrospective analysis of the CARM pharmacovigilance database operated by the New Zealand Pharmacovigilance Centre was undertaken for the year 1 January-31 December 2007. All reports, excluding those relating to vaccines, clinical trials and pharmaceutical company reports, underwent a preventability assessment using predetermined criteria. Those events deemed preventable were subsequently classified to identify the degree of patient harm, type of error, stage of medication use process where the error occurred and origin of the error. A total of 1412 reports met the inclusion criteria and were reviewed, of which 4.3% (61/1412) were deemed preventable. Not all errors resulted in patient harm: 29.5% (18/61) were 'no harm' errors but 65.5% (40/61) of errors were deemed to have been associated with some degree of patient harm (preventable adverse drug events [ADEs]). For 5.0% (3/61) of events, the degree of patient harm was unable to be determined as the patient outcome was unknown. The majority of preventable ADEs (62.5% [25/40]) occurred in adults aged 65 years and older. The medication classes most involved in preventable ADEs were antibacterials for systemic use and anti-inflammatory agents, with gastrointestinal and respiratory system disorders the most common adverse events reported. For both preventable ADEs and 'no harm' events, most errors were incorrect dose and drug therapy monitoring problems consisting of failures in detection of significant drug interactions, past allergies or lack of necessary clinical monitoring. Preventable events were mostly related to the prescribing and administration stages of the medication use process, with the majority of errors 82.0% (50/61) deemed to have originated in the community setting. The CARM pharmacovigilance database includes medication errors, many of which were found to originate in the community setting and reported as ADRs. Error-prone situations were able to be identified, providing greater opportunity to improve patient safety. However, to enhance detection of medication errors by pharmacovigilance centres, reports should be prospectively reviewed for preventability and the reporting form revised to facilitate capture of important information that will provide meaningful insight into the nature of the underlying systems defects that caused the error.

Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips

PubMed Central

Stec, James; Wang, Jing; Coombes, Kevin; Ayers, Mark; Hoersch, Sebastian; Gold, David L.; Ross, Jeffrey S; Hess, Kenneth R.; Tirrell, Stephen; Linette, Gerald; Hortobagyi, Gabriel N.; Symmans, W. Fraser; Pusztai, Lajos

2005-01-01

We examined how well differentially expressed genes and multigene outcome classifiers retain their class-discriminating values when tested on data generated by different transcriptional profiling platforms. RNA from 33 stage I-III breast cancers was hybridized to both Affymetrix GeneChip and Millennium Pharmaceuticals cDNA arrays. Only 30% of all corresponding gene expression measurements on the two platforms had Pearson correlation coefficient r ≥ 0.7 when UniGene was used to match probes. There was substantial variation in correlation between different Affymetrix probe sets matched to the same cDNA probe. When cDNA and Affymetrix probes were matched by basic local alignment tool (BLAST) sequence identity, the correlation increased substantially. We identified 182 genes in the Affymetrix and 45 in the cDNA data (including 17 common genes) that accurately separated 91% of cases in supervised hierarchical clustering in each data set. Cross-platform testing of these informative genes resulted in lower clustering accuracy of 45 and 79%, respectively. Several sets of accurate five-gene classifiers were developed on each platform using linear discriminant analysis. The best 100 classifiers showed average misclassification error rate of 2% on the original data that rose to 19.5% when tested on data from the other platform. Random five-gene classifiers showed misclassification error rate of 33%. We conclude that multigene predictors optimized for one platform lose accuracy when applied to data from another platform due to missing genes and sequence differences in probes that result in differing measurements for the same gene. PMID:16049308

Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Volpi, Michele; Copa, Loris

2010-05-01

The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of MNO problem: 1) hierarchical top-down clustering in an input space in order to remove redundancy when data are clustered, and 2) a general method (independent on classifier) which gives posterior probabilities that can be used to define the classifier confidence and corresponding proposals for new measurement points. The basic ideas and procedures are explained by applying simulated data sets. The real case study deals with the analysis and mapping of soil types, which is a multi-class classification problem. Maps of soil types are important for the analysis and 3D modeling of heavy metals migration in soil and prediction risk mapping. The results obtained demonstrate the high quality of SVM mapping and efficiency of monitoring network optimization by using active learning approaches. The research was partly supported by SNSF projects No. 200021-126505 and 200020-121835.

The Application of Speaker Recognition Techniques in the Detection of Tsunamigenic Earthquakes

NASA Astrophysics Data System (ADS)

Gorbatov, A.; O'Connell, J.; Paliwal, K.

2015-12-01

Tsunami warning procedures adopted by national tsunami warning centres largely rely on the classical approach of earthquake location, magnitude determination, and the consequent modelling of tsunami waves. Although this approach is based on known physics theories of earthquake and tsunami generation processes, this may be the main shortcoming due to the need to satisfy minimum seismic data requirement to estimate those physical parameters. At least four seismic stations are necessary to locate the earthquake and a minimum of approximately 10 minutes of seismic waveform observation to reliably estimate the magnitude of a large earthquake similar to the 2004 Indian Ocean Tsunami Earthquake of M9.2. Consequently the total time to tsunami warning could be more than half an hour. In attempt to reduce the time of tsunami alert a new approach is proposed based on the classification of tsunamigenic and non tsunamigenic earthquakes using speaker recognition techniques. A Tsunamigenic Dataset (TGDS) was compiled to promote the development of machine learning techniques for application to seismic trace analysis and, in particular, tsunamigenic event detection, and compare them to existing seismological methods. The TGDS contains 227 off shore events (87 tsunamigenic and 140 non-tsunamigenic earthquakes with M≥6) from Jan 2000 to Dec 2011, inclusive. A Support Vector Machine classifier using a radial-basis function kernel was applied to spectral features derived from 400 sec frames of 3-comp. 1-Hz broadband seismometer data. Ten-fold cross-validation was used during training to choose classifier parameters. Voting was applied to the classifier predictions provided from each station to form an overall prediction for an event. The F1 score (harmonic mean of precision and recall) was chosen to rate each classifier as it provides a compromise between type-I and type-II errors, and due to the imbalance between the representative number of events in the tsunamigenic and non-tsunamigenic classes. The described classifier achieved an F1 score of 0.923, with tsunamigenic classification precision and recall/sensitivity of 0.928 and 0.919 respectively. The system requires a minimum of 3 stations with ~400 seconds of data each to make a prediction. The accuracy improves as further stations and data become available.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

Towards a robust BCI: error potentials and online learning.

PubMed

Buttfield, Anna; Ferrez, Pierre W; Millán, José del R

2006-06-01

Recent advances in the field of brain-computer interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful laboratory demonstrations, there are still issues that need to be addressed before BCIs can be used by nonexperts outside the laboratory. At IDIAP Research Institute, we have been investigating several areas that we believe will allow us to improve the robustness, flexibility, and reliability of BCIs. One area is recognition of cognitive error states, that is, identifying errors through the brain's reaction to mistakes. The production of these error potentials (ErrP) in reaction to an error made by the user is well established. We have extended this work by identifying a similar but distinct ErrP that is generated in response to an error made by the interface, (a misinterpretation of a command that the user has given). This ErrP can be satisfactorily identified in single trials and can be demonstrated to improve the theoretical performance of a BCI. A second area of research is online adaptation of the classifier. BCI signals change over time, both between sessions and within a single session, due to a number of factors. This means that a classifier trained on data from a previous session will probably not be optimal for a new session. In this paper, we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase. We also discuss the future direction of this research, including the combination of these two currently separate issues to create a potentially very powerful BCI.

An epidemiologic survey of road traffic accidents in Iran: analysis of driver-related factors.

PubMed

Moafian, Ghasem; Aghabeigi, Mohammad-Reza; Heydari, Seyed Taghi; Hoseinzadeh, Amin; Lankarani, Kamran Bagheri; Sarikhani, Yaser

2013-01-01

Road traffic accident (RTA) and its related injuries contribute to a significant portion of the burden of diseases in Iran. This paper explores the association between driver-related factors and RTA in the country. This cross-sectional study was conducted in Iran and all data regarding RTAs from March 20, 2010 to June 10, 2010 were obtained from the Traffic Police Department. We included 538 588 RTA records, which were classified to control for the main confounders: accident type, final cause of accident, time of accident and driver-related factors. Driver-related factors included sex, educational level, license type, type of injury, duration between accident and getting the driving license and driver's error type. A total of 538 588 drivers (91.83% male, sex ratio of almost 13:1) were involved in the RTAs. Among them 423 932 (78.71%) were uninjured; 224 818 (41.74%) had a diploma degree. Grade 2 driving license represented the highest proportion of all driving licenses (290 811, 54.00%). The greatest number of accidents took place at 12:00-13:59 (75 024, 13.93%). The proportion of drivers involved in RTAs decreased from 15.90% in the first year of getting a driving license to 3.13% after 10 years'of driving experience. Neglect of regulations was the commonest cause of traffic crashes (345 589, 64.17%). Non-observance of priority and inattention to the front were the most frequent final causes of death (138 175, 25.66% and 129 352, 24.02%, respectively). We found significant association between type of accident and sex, education, license type, time of accident, final cause of accident, driver's error as well as duration between accident and getting the driving license (all P less than 0.001). Our results will improve the traffic law enforcement measures, which will change inappropriate behavior of drivers and protect the least experienced road users.

A descriptive model of preventability in maternal morbidity and mortality.

PubMed

Geller, S E; Cox, S M; Kilpatrick, S J

2006-02-01

To develop a descriptive model of preventability for maternal morbidity and mortality that can be used in quality assurance and morbidity and mortality review processes. This descriptive study was part of a larger case-control study conducted at the University of Illinois at Chicago in which maternal deaths were cases and women with severe maternal morbidity served as controls. Morbidities and mortalities were classified by a team of clinicians as preventable or not preventable. Qualitative analysis of data was conducted to identify and categorize different types of preventable events. Of 237 women, there were 79 women with preventable events attributable to provider or system factors. The most common types of preventable events were inadequate diagnosis/recognition of high-risk (54.4%), treatment (38.0%), and documentation (30.7%). A descriptive model was illustrated that can be used to categorize preventable events in maternal morbidity and mortality and can be incorporated into quality assurance and clinical case review to enhance the monitoring of hospital-based obstetric care and to decrease medical error.

Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

NASA Technical Reports Server (NTRS)

Craig, R. G. (Principal Investigator)

1983-01-01

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Structural analysis of online handwritten mathematical symbols based on support vector machines

NASA Astrophysics Data System (ADS)

Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

2013-01-01

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

Application of Snyder-Dolan classification scheme to the selection of "orthogonal" columns for fast screening of illicit drugs and impurity profiling of pharmaceuticals--I. Isocratic elution.

PubMed

Fan, Wenzhe; Zhang, Yu; Carr, Peter W; Rutan, Sarah C; Dumarey, Melanie; Schellinger, Adam P; Pritts, Wayne

2009-09-18

Fourteen judiciously selected reversed phase columns were tested with 18 cationic drug solutes under the isocratic elution conditions advised in the Snyder-Dolan (S-D) hydrophobic subtraction method of column classification. The standard errors (S.E.) of the least squares regressions of logk' vs. logk'(REF) were obtained for a given column against a reference column and used to compare and classify columns based on their selectivity. The results are consistent with those obtained with a study of the 16 test solutes recommended by Snyder and Dolan. To the extent these drugs are representative, these results show that the S-D classification scheme is also generally applicable to pharmaceuticals under isocratic conditions. That is, those columns judged to be similar based on the 16 S-D solutes were similar based on the 18 drugs; furthermore those columns judged to have significantly different selectivities based on the 16 S-D probes appeared to be quite different for the drugs as well. Given that the S-D method has been used to classify more than 400 different types of reversed phases the extension to cationic drugs is a significant finding.

Transfer learning for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Benson, Jeremy; Carrillo, Hector; Wigdahl, Jeff; Nemeth, Sheila; Maynard, John; Zamora, Gilberto; Barriga, Simon; Estrada, Trilce; Soliz, Peter

2018-03-01

Diabetic Retinopathy (DR)1, 2 is a leading cause of blindness worldwide and is estimated to threaten the vision of nearly 200 million by 2030.3 To work with the ever-increasing population, the use of image processing algorithms to screen for those at risk has been on the rise. Research-oriented solutions have proven effective in classifying images with or without DR, but often fail to address the true need of the clinic - referring only those who need to be seen by a specialist, and reading every single case. In this work, we leverage an array of image pre-preprocessing techniques, as well as Transfer Learning to re-purpose an existing deep network for our tasks in DR. We train, test, and validate our system on 979 clinical cases, achieving a 95% Area Under the Curve (AUC) for referring Severe DR with an equal error Sensitivity and Specificity of 90%. Our system does not reject any images based on their quality, and is agnostic in terms of eye side and field. These results show that general purpose classifiers can, with the right type of input, have a major impact in clinical environments or for teams lacking access to large volumes of data or high-throughput supercomputers.

Identifying types and causes of errors in mortality data in a clinical registry using multiple information systems.

PubMed

Koetsier, Antonie; Peek, Niels; de Keizer, Nicolette

2012-01-01

Errors may occur in the registration of in-hospital mortality, making it less reliable as a quality indicator. We assessed the types of errors made in in-hospital mortality registration in the clinical quality registry National Intensive Care Evaluation (NICE) by comparing its mortality data to data from a national insurance claims database. Subsequently, we performed site visits at eleven Intensive Care Units (ICUs) to investigate the number, types and causes of errors made in in-hospital mortality registration. A total of 255 errors were found in the NICE registry. Two different types of software malfunction accounted for almost 80% of the errors. The remaining 20% were five types of manual transcription errors and human failures to record outcome data. Clinical registries should be aware of the possible existence of errors in recorded outcome data and understand their causes. In order to prevent errors, we recommend to thoroughly verify the software that is used in the registration process.

[Patient safety and errors in medicine: development, prevention and analyses of incidents].

PubMed

Rall, M; Manser, T; Guggenberger, H; Gaba, D M; Unertl, K

2001-06-01

"Patient safety" and "errors in medicine" are issues gaining more and more prominence in the eyes of the public. According to newer studies, errors in medicine are among the ten major causes of death in association with the whole area of health care. A new era has begun incorporating attention to a "systems" approach to deal with errors and their causes in the health system. In other high-risk domains with a high demand for safety (such as the nuclear power industry and aviation) many strategies to enhance safety have been established. It is time to study these strategies, to adapt them if necessary and apply them to the field of medicine. These strategies include: to teach people how errors evolve in complex working domains and how types of errors are classified; the introduction of critical incident reporting systems that are free of negative consequences for the reporters; the promotion of continuous medical education; and the development of generic problem-solving skills incorporating the extensive use of realistic simulators wherever possible. Interestingly, the field of anesthesiology--within which realistic simulators were developed--is referred to as a model for the new patient safety movement. Despite this proud track record in recent times though, there is still much to be done even in the field of anesthesiology. Overall though, the most important strategy towards a long-term improvement in patient safety will be a change of "culture" throughout the entire health care system. The "culture of blame" focused on individuals should be replaced by a "safety culture", that sees errors and critical incidents as a problem of the whole organization. The acceptance of human fallability and an open-minded non-punitive analysis of errors in the sense of a "preventive and proactive safety culture" should lead to solutions at the systemic level. This change in culture can only be achieved with a strong commitment from the highest levels of an organization. Patient safety must have the highest priority in the goals of the institution: "Primum nihil nocere"--"First, do not harm".

Diagnostic Error in Correctional Mental Health: Prevalence, Causes, and Consequences.

PubMed

Martin, Michael S; Hynes, Katie; Hatcher, Simon; Colman, Ian

2016-04-01

While they have important implications for inmates and resourcing of correctional institutions, diagnostic errors are rarely discussed in correctional mental health research. This review seeks to estimate the prevalence of diagnostic errors in prisons and jails and explores potential causes and consequences. Diagnostic errors are defined as discrepancies in an inmate's diagnostic status depending on who is responsible for conducting the assessment and/or the methods used. It is estimated that at least 10% to 15% of all inmates may be incorrectly classified in terms of the presence or absence of a mental illness. Inmate characteristics, relationships with staff, and cognitive errors stemming from the use of heuristics when faced with time constraints are discussed as possible sources of error. A policy example of screening for mental illness at intake to prison is used to illustrate when the risk of diagnostic error might be increased and to explore strategies to mitigate this risk. © The Author(s) 2016.

Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier

PubMed Central

Porter, Teresita M; Gibson, Joel F; Shokralla, Shadi; Baird, Donald J; Golding, G Brian; Hajibabaei, Mehrdad

2014-01-01

Current methods to identify unknown insect (class Insecta) cytochrome c oxidase (COI barcode) sequences often rely on thresholds of distances that can be difficult to define, sequence similarity cut-offs, or monophyly. Some of the most commonly used metagenomic classification methods do not provide a measure of confidence for the taxonomic assignments they provide. The aim of this study was to use a naïve Bayesian classifier (Wang et al. Applied and Environmental Microbiology, 2007; 73: 5261) to automate taxonomic assignments for large batches of insect COI sequences such as data obtained from high-throughput environmental sequencing. This method provides rank-flexible taxonomic assignments with an associated bootstrap support value, and it is faster than the blast-based methods commonly used in environmental sequence surveys. We have developed and rigorously tested the performance of three different training sets using leave-one-out cross-validation, two field data sets, and targeted testing of Lepidoptera, Diptera and Mantodea sequences obtained from the Barcode of Life Data system. We found that type I error rates, incorrect taxonomic assignments with a high bootstrap support, were already relatively low but could be lowered further by ensuring that all query taxa are actually present in the reference database. Choosing bootstrap support cut-offs according to query length and summarizing taxonomic assignments to more inclusive ranks can also help to reduce error while retaining the maximum number of assignments. Additionally, we highlight gaps in the taxonomic and geographic representation of insects in public sequence databases that will require further work by taxonomists to improve the quality of assignments generated using any method.

Models for H₃ receptor antagonist activity of sulfonylurea derivatives.

PubMed

Khatri, Naveen; Madan, A K

2014-03-01

The histamine H₃ receptor has been perceived as an auspicious target for the treatment of various central and peripheral nervous system diseases. In present study, a wide variety of 60 2D and 3D molecular descriptors (MDs) were successfully utilized for the development of models for the prediction of antagonist activity of sulfonylurea derivatives for histamine H₃ receptors. Models were developed through decision tree (DT), random forest (RF) and moving average analysis (MAA). Dragon software version 6.0.28 was employed for calculation of values of diverse MDs of each analogue involved in the data set. The DT classified and correctly predicted the input data with an impressive non-error rate of 94% in the training set and 82.5% during cross validation. RF correctly classified the analogues into active and inactive with a non-error rate of 79.3%. The MAA based models predicted the antagonist histamine H₃ receptor activity with non-error rate up to 90%. Active ranges of the proposed MAA based models not only exhibited high potency but also showed improved safety as indicated by relatively high values of selectivity index. The statistical significance of the models was assessed through sensitivity, specificity, non-error rate, Matthew's correlation coefficient and intercorrelation analysis. Proposed models offer vast potential for providing lead structures for development of potent but safe H₃ receptor antagonist sulfonylurea derivatives. Copyright © 2013 Elsevier Inc. All rights reserved.

Automated Detection and Annotation of Disturbance in Eastern Forests

NASA Astrophysics Data System (ADS)

Hughes, M. J.; Chen, G.; Hayes, D. J.

2013-12-01

Forest disturbances represent an important component of the terrestrial carbon budget. To generate spatially-explicit estimates of disturbance and regrowth, we developed an automated system to detect and characterize forest change in the eastern United States at 30 m resolution from a 28-year Landsat Thematic Mapper time-series (1984-2011). Forest changes are labeled as 'disturbances' or 'regrowth', assigned to a severity class, and attributed to a disturbance type: either fire, insects, harvest, or 'unknown'. The system generates cloud-free summertime composite images for each year from multiple summer scenes and calculates vegetation indices from these composites. Patches of similar terrain on the landscape are identified by segmenting the Normalized Burn Ratio image. The spatial variance within each patch, which has been found to be a good indicator of diffuse disturbances such as forest insect damage, is then calculated for each index, creating an additional set of indexes. To identify vegetation change and quantify its degree, the derivative through time is calculated for each index using total variance regularization to account for noise and create a piecewise-linear trend. These indexes and their derivatives detect areas of disturbance and regrowth and are also used as inputs into a neural network that classifies the disturbance type/agent. Disturbance and disease information from the US Forest Service Aerial Detection Surveys (ADS) geodatabase and disturbed plots from the US Forest Service Forest Inventory and Analysis (FIA) database provided training data for the neural network. Although there have been recent advances in discriminating between disturbance types in boreal forests, due to the larger number of forest species and cosmopolitan nature of overstory communities in eastern forests, separation remains difficult. The ADS database, derived from sketch maps and later digitized, commonly designates a single large area encompassing many smaller effected areas as disturbed, overestimating disturbance and creating ambiguity in the neural network. Even so, total classification error in a neighboring testing region is 22%. Most error comes labeling disturbances that are unknown in the training data as a known disturbance type. Confusion within known disturbance types is low, with 7% misclassification error for southern pine beetle, and 11% misclassification error for fire, which is likely due to over-estimates of disturbance extent in ADS polygons. Additionally, we used the Terrestrial Ecosystem Model (TEM) to quantify the carbon flux associated with a subset of selected disturbances of different severity and type. Early results show that combined disturbances resulted in a net carbon source of 1.27 kg/m2 between 1981 and 2010, which is about 8% of the total carbon storage in forests. This carbon loss offset much of the carbon sink effects resulting from elevated atmospheric CO2 and nitrogen deposition.

An expert computer program for classifying stars on the MK spectral classification system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gray, R. O.; Corbally, C. J.

2014-04-01

This paper describes an expert computer program (MKCLASS) designed to classify stellar spectra on the MK Spectral Classification system in a way similar to humans—by direct comparison with the MK classification standards. Like an expert human classifier, the program first comes up with a rough spectral type, and then refines that spectral type by direct comparison with MK standards drawn from a standards library. A number of spectral peculiarities, including barium stars, Ap and Am stars, λ Bootis stars, carbon-rich giants, etc., can be detected and classified by the program. The program also evaluates the quality of the delivered spectralmore » type. The program currently is capable of classifying spectra in the violet-green region in either the rectified or flux-calibrated format, although the accuracy of the flux calibration is not important. We report on tests of MKCLASS on spectra classified by human classifiers; those tests suggest that over the entire HR diagram, MKCLASS will classify in the temperature dimension with a precision of 0.6 spectral subclass, and in the luminosity dimension with a precision of about one half of a luminosity class. These results compare well with human classifiers.« less

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

PubMed Central

Kolluru, BalaKrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. PMID:21633495

Using workflows to explore and optimise named entity recognition for chemistry.

PubMed

Kolluru, Balakrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR.

Deep learning model-based algorithm for SAR ATR

NASA Astrophysics Data System (ADS)

Friedlander, Robert D.; Levy, Michael; Sudkamp, Elizabeth; Zelnio, Edmund

2018-05-01

Many computer-vision-related problems have successfully applied deep learning to improve the error rates with respect to classifying images. As opposed to optically based images, we have applied deep learning via a Siamese Neural Network (SNN) to classify synthetic aperture radar (SAR) images. This application of Automatic Target Recognition (ATR) utilizes an SNN made up of twin AlexNet-based Convolutional Neural Networks (CNNs). Using the processing power of GPUs, we trained the SNN with combinations of synthetic images on one twin and Moving and Stationary Target Automatic Recognition (MSTAR) measured images on a second twin. We trained the SNN with three target types (T-72, BMP2, and BTR-70) and have used a representative, synthetic model from each target to classify new SAR images. Even with a relatively small quantity of data (with respect to machine learning), we found that the SNN performed comparably to a CNN and had faster convergence. The results of processing showed the T-72s to be the easiest to identify, whereas the network sometimes mixed up the BMP2s and the BTR-70s. In addition we also incorporated two additional targets (M1 and M35) into the validation set. Without as much training (for example, one additional epoch) the SNN did not produce the same results as if all five targets had been trained over all the epochs. Nevertheless, an SNN represents a novel and beneficial approach to SAR ATR.

Automatic tissue characterization from ultrasound imagery

NASA Astrophysics Data System (ADS)

Kadah, Yasser M.; Farag, Aly A.; Youssef, Abou-Bakr M.; Badawi, Ahmed M.

1993-08-01

In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Inductive Selectivity in Children's Cross-Classified Concepts

ERIC Educational Resources Information Center

Nguyen, Simone P.

2012-01-01

Cross-classified items pose an interesting challenge to children's induction as these items belong to many different categories, each of which may serve as a basis for a different type of inference. Inductive selectivity is the ability to appropriately make different types of inferences about a single cross-classifiable item based on its different…

Development of a coding form for approach control/pilot voice communications.

DOT National Transportation Integrated Search

1995-05-01

The Aviation Topics Speech Acts Taxonomy (ATSAT) is a tool for categorizing pilot/controller communications according to their purpose and for classifying communication errors. Air traffic controller communications that deviate from FAA Air Traffic C...

Utilization of aerial laser scanning data in investigations of modern fortifications complexes in Poland. (Polish Title: Wykorzystanie danych lotniczego skaningu laserowego w metodyce badawczej zespołów fortyfikacji nowszej w Polsce)

NASA Astrophysics Data System (ADS)

Zawieska, D.; Ostrowski, W.; Antoszewski, M.

2013-12-01

Due to the turbulent history extremely reach and unique resources of military architectural objects (modern fortification complexes) are located in Poland. The paper presents results of analysis of utilization of aerial laser scanning data for identification and visualization of forts in Poland. A cloud of point from the ISOK Projects has been utilized for that purpose. Two types of areas are distinguished in this Project, covered by products of diversified standards: standards II - laser scanning of the increased density (12 points per sq.m.), standard I - laser scanning of the basic density (4 points per sq.m.). Investigations were carried out concerning the quality of geospatial data classification with respect to further topographic analysis of fortifications. These investigations were performed for four test sites, two test sites for each standard. Objects were selected in such a way that fortifications were characterized by the sufficient level of restoration and that at least one point located in forest and one point located in an open area could be located for each standard. The preliminary verification of the classification correctness was performed with the use of ArcGIS 10.1 software package, basing on the shaded Digital Elevation Model (DEM) and the Digital Fortification Model (DFM), an orthophotomap and the analysis of sections of the spatial cloud of points. Changes of classification of point clouds were introduced with the use of TerraSolid software package. Basing on the performed analysis two groups of errors of point cloud classification were detected. In the first group fragments of fortification facilities were classified with errors; in the case of the second group - entire elements of fortifications were classified with errors or they remained unclassified. The first type error, which occurs in the majority of cases, results in errors of 2x4 meters in object locations and variations of elevations of those fragments of DFM, which achieve up to 14 m. At present, fortifications are partially or entirely covered with forests or invasive vegetation. Therefore, the influence of the land cover and the terrain slope on the DEM quality, obtained from Lidar data, should be considered in evaluation of the ISOK data potential for topographic investigations of fortifications. Investigations performed in the world proved that if the area is covered by dense, 70 year old forests, where forest clearance is not performed, this may result in double decrease of the created DTM. (comparing to the open area). In the summary it may be stressed that performed experimental works proved the high usefulness of ISOK laser scanning data for identification of forms of fortifications and for their visualization. As opposed to conventional information acquisition methods (field inventory together with historical documents), laser scanning data is the new generation of geospatial data. They create the possibility to develop the new technology, to be utilized in protection and inventory of military architectural objects in Poland.

Validation of VITEK 2 Version 4.01 Software for Detection, Identification, and Classification of Glycopeptide-Resistant Enterococci

PubMed Central

Abele-Horn, Marianne; Hommers, Leif; Trabold, René; Frosch, Matthias

2006-01-01

We evaluated the ability of the new VITEK 2 version 4.01 software to identify and detect glycopeptide-resistant enterococci compared to that of the reference broth microdilution method and to classify them into the vanA, vanB, vanC1, and vanC2 genotypes. Moreover, the accuracy of antimicrobial susceptibility testing with agents with improved potencies against glycopeptide-resistant enterococci was determined. A total of 121 enterococci were investigated. The new VITEK 2 software was able to identify 114 (94.2%) enterococcal strains correctly to the species level and to classify 119 (98.3%) enterococci correctly to the glycopeptide resistance genotype level. One Enterococcus casseliflavus strain and six Enterococcus faecium vanA strains with low-level resistance to vancomycin were identified with low discrimination, requiring additional tests. One of the vanA strains was misclassified as the vanB type, and one glycopeptide-susceptible E. facium wild type was misclassified as the vanA type. The overall essential agreements for antimicrobial susceptibility testing results were 94.2% for vancomycin, 95.9% for teicoplanin, 100% for quinupristin-dalfopristin and moxifloxacin, and 97.5% for linezolid. The rates of minor errors were 9% for teicoplanin and 5% for the other antibiotic agents. The identification and susceptibility data were produced within 4 h to 6 h 30 min and 8 h 15 min to 12 h 15 min. In conclusion, use of VITEK 2 version 4.01 software appears to be a reliable method for the identification and detection of glycopeptide-resistant enterococci as well as an improvement over the use of the former VITEK 2 database. However, a significant reduction in the detection time would be desirable. PMID:16390951

The Relationship between Occurrence Timing of Dispensing Errors and Subsequent Danger to Patients under the Situation According to the Classification of Drugs by Efficacy.

PubMed

Tsuji, Toshikazu; Nagata, Kenichiro; Kawashiri, Takehiro; Yamada, Takaaki; Irisa, Toshihiro; Murakami, Yuko; Kanaya, Akiko; Egashira, Nobuaki; Masuda, Satohiro

2016-01-01

There are many reports regarding various medical institutions' attempts at the prevention of dispensing errors. However, the relationship between occurrence timing of dispensing errors and subsequent danger to patients has not been studied under the situation according to the classification of drugs by efficacy. Therefore, we analyzed the relationship between position and time regarding the occurrence of dispensing errors. Furthermore, we investigated the relationship between occurrence timing of them and danger to patients. In this study, dispensing errors and incidents in three categories (drug name errors, drug strength errors, drug count errors) were classified into two groups in terms of its drug efficacy (efficacy similarity (-) group, efficacy similarity (+) group), into three classes in terms of the occurrence timing of dispensing errors (initial phase errors, middle phase errors, final phase errors). Then, the rates of damage shifting from "dispensing errors" to "damage to patients" were compared as an index of danger between two groups and among three classes. Consequently, the rate of damage in "efficacy similarity (-) group" was significantly higher than that in "efficacy similarity (+) group". Furthermore, the rate of damage is the highest in "initial phase errors", the lowest in "final phase errors" among three classes. From the results of this study, it became clear that the earlier the timing of dispensing errors occurs, the more severe the damage to patients becomes.

A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS

PubMed Central

Benza, Magdalena; Weeks, John R.; Stow, Douglas A.; López-Carr, David; Clarke, Keith C.

2016-01-01

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user’s accuracy (commission errors) being lower than producer’s accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient. PMID:27867227

A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS.

PubMed

Benza, Magdalena; Weeks, John R; Stow, Douglas A; López-Carr, David; Clarke, Keith C

2016-09-15

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.

Remediating Common Math Errors.

ERIC Educational Resources Information Center

Wagner, Rudolph F.

1981-01-01

Explanations and remediation suggestions for five types of mathematics errors due either to perceptual or cognitive difficulties are given. Error types include directionality problems, mirror writing, visually misperceived signs, diagnosed directionality problems, and mixed process errors. (CL)

Imagery of Errors in Typing

ERIC Educational Resources Information Center

Rieger, Martina; Martinez, Fanny; Wenke, Dorit

2011-01-01

Using a typing task we investigated whether insufficient imagination of errors and error corrections is related to duration differences between execution and imagination. In Experiment 1 spontaneous error imagination was investigated, whereas in Experiment 2 participants were specifically instructed to imagine errors. Further, in Experiment 2 we…

Heuristic errors in clinical reasoning.

PubMed

Rylander, Melanie; Guerrasio, Jeannette

2016-08-01

Errors in clinical reasoning contribute to patient morbidity and mortality. The purpose of this study was to determine the types of heuristic errors made by third-year medical students and first-year residents. This study surveyed approximately 150 clinical educators inquiring about the types of heuristic errors they observed in third-year medical students and first-year residents. Anchoring and premature closure were the two most common errors observed amongst third-year medical students and first-year residents. There was no difference in the types of errors observed in the two groups. Errors in clinical reasoning contribute to patient morbidity and mortality Clinical educators perceived that both third-year medical students and first-year residents committed similar heuristic errors, implying that additional medical knowledge and clinical experience do not affect the types of heuristic errors made. Further work is needed to help identify methods that can be used to reduce heuristic errors early in a clinician's education. © 2015 John Wiley & Sons Ltd.

Classification and reduction of pilot error

NASA Technical Reports Server (NTRS)

Rogers, W. H.; Logan, A. L.; Boley, G. D.

1989-01-01

Human error is a primary or contributing factor in about two-thirds of commercial aviation accidents worldwide. With the ultimate goal of reducing pilot error accidents, this contract effort is aimed at understanding the factors underlying error events and reducing the probability of certain types of errors by modifying underlying factors such as flight deck design and procedures. A review of the literature relevant to error classification was conducted. Classification includes categorizing types of errors, the information processing mechanisms and factors underlying them, and identifying factor-mechanism-error relationships. The classification scheme developed by Jens Rasmussen was adopted because it provided a comprehensive yet basic error classification shell or structure that could easily accommodate addition of details on domain-specific factors. For these purposes, factors specific to the aviation environment were incorporated. Hypotheses concerning the relationship of a small number of underlying factors, information processing mechanisms, and error types types identified in the classification scheme were formulated. ASRS data were reviewed and a simulation experiment was performed to evaluate and quantify the hypotheses.

Accuracy of reported flash point values on material safety data sheets and the impact on product classification.

PubMed

Radnoff, Diane

2013-01-01

Material Safety Data Sheets (MSDSs) are the foundation of worker right-to-know legislation for chemical hazards. Suppliers can use product test data to determine a product's classification. Alternatively, they may use evaluation and professional judgment based on test results for the product or a product, material, or substance with similar properties. While the criteria for classifying products under the new Globally Harmonized System of Classification and Labeling of Chemicals (GHS) are different, a similar process is followed. Neither the current Workplace Hazardous Materials Information System (WHMIS) nor GHS require suppliers to test their products to classify them. In this project 83 samples of products classified as flammable or combustible, representing a variety of industry sectors and product types, were collected. Flash points were measured and compared to the reported values on the MSDSs. The classifications of the products were then compared using the WHMIS and GHS criteria. The results of the study indicated that there were significant variations between the disclosed and measured flash point values. Overall, more than one-third of the products had flash points lower than that disclosed on the MSDS. In some cases, the measured values were more than 20°C lower than the disclosed values. This could potentially result in an underestimation regarding the flammability of the product so it is important for employers to understand the limitations in the information provided on MSDSs when developing safe work procedures and training programs in the workplace. Nearly one-fifth of the products were misclassified under the WHMIS system as combustible when the measured flash point indicated that they should be classified as flammable when laboratory measurement error was taken into account. While a similar number of products were misclassified using GHS criteria, the tendency appeared to be to "over-classify" (provide a hazard class that was more conservative). So the transition to GHS may potentially decrease the possibility of "under-classifying" flammable and combustible products where no test data on the product are available.

Residents' numeric inputting error in computerized physician order entry prescription.

PubMed

Wu, Xue; Wu, Changxu; Zhang, Kan; Wei, Dong

2016-04-01

Computerized physician order entry (CPOE) system with embedded clinical decision support (CDS) can significantly reduce certain types of prescription error. However, prescription errors still occur. Various factors such as the numeric inputting methods in human computer interaction (HCI) produce different error rates and types, but has received relatively little attention. This study aimed to examine the effects of numeric inputting methods and urgency levels on numeric inputting errors of prescription, as well as categorize the types of errors. Thirty residents participated in four prescribing tasks in which two factors were manipulated: numeric inputting methods (numeric row in the main keyboard vs. numeric keypad) and urgency levels (urgent situation vs. non-urgent situation). Multiple aspects of participants' prescribing behavior were measured in sober prescribing situations. The results revealed that in urgent situations, participants were prone to make mistakes when using the numeric row in the main keyboard. With control of performance in the sober prescribing situation, the effects of the input methods disappeared, and urgency was found to play a significant role in the generalized linear model. Most errors were either omission or substitution types, but the proportion of transposition and intrusion error types were significantly higher than that of the previous research. Among numbers 3, 8, and 9, which were the less common digits used in prescription, the error rate was higher, which was a great risk to patient safety. Urgency played a more important role in CPOE numeric typing error-making than typing skills and typing habits. It was recommended that inputting with the numeric keypad had lower error rates in urgent situation. An alternative design could consider increasing the sensitivity of the keys with lower frequency of occurrence and decimals. To improve the usability of CPOE, numeric keyboard design and error detection could benefit from spatial incidence of errors found in this study. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Assessment of 3D cloud radiative transfer effects applied to collocated A-Train data

NASA Astrophysics Data System (ADS)

Okata, M.; Nakajima, T.; Suzuki, K.; Toshiro, I.; Nakajima, T. Y.; Okamoto, H.

2017-12-01

This study investigates broadband radiative fluxes in the 3D cloud-laden atmospheres using a 3D radiative transfer (RT) model, MCstar, and the collocated A-Train cloud data. The 3D extinction coefficients are constructed by a newly devised Minimum cloud Information Deviation Profiling Method (MIDPM) that extrapolates CPR radar profiles at nadir into off-nadir regions within MODIS swath based on collocated information of MODIS-derived cloud properties and radar reflectivity profiles. The method is applied to low level maritime water clouds, for which the 3D-RT simulations are performed. The radiative fluxes thus simulated are compared to those obtained from CERES as a way to validate the MIDPM-constructed clouds and our 3D-RT simulations. The results show that the simulated SW flux agrees with CERES values within 8 - 50 Wm-2. One of the large biases occurred by cyclic boundary condition that was required to pose into our computational domain limited to 20km by 20km with 1km resolution. Another source of the bias also arises from the 1D assumption for cloud property retrievals particularly for thin clouds, which tend to be affected by spatial heterogeneity leading to overestimate of the cloud optical thickness. These 3D-RT simulations also serve to address another objective of this study, i.e. to characterize the "observed" specific 3D-RT effects by the cloud morphology. We extend the computational domain to 100km by 100km for this purpose. The 3D-RT effects are characterized by errors of existing 1D approximations to 3D radiation field. The errors are investigated in terms of their dependence on solar zenith angle (SZA) for the satellite-constructed real cloud cases, and we define two indices from the error tendencies. According to the indices, the 3D-RT effects are classified into three types which correspond to different simple three morphologies types, i.e. isolated cloud type, upper cloud-roughened type and lower cloud-roughened type. These 3D-RT effects linked to cloud morphologies are also visualized in the form of the RGB composite maps constructed from MODIS/Aqua three channels, which show cloud optical thickness and cloud height information. Such a classification offers a novel insight into 3D-RT effect in a manner that directly relates to cloud morphology.

Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system.

PubMed

Shappell, Scott; Detwiler, Cristy; Holcomb, Kali; Hackworth, Carla; Boquet, Albert; Wiegmann, Douglas A

2007-04-01

The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS). HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents. Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period. The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences. These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety. HFACS provides a tool for assessing human factors associated with accidents and incidents.

Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Classifier fusion for VoIP attacks classification

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Rezac, Filip

2017-05-01

SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

The (mis)reporting of statistical results in psychology journals.

PubMed

Bakker, Marjan; Wicherts, Jelte M

2011-09-01

In order to study the prevalence, nature (direction), and causes of reporting errors in psychology, we checked the consistency of reported test statistics, degrees of freedom, and p values in a random sample of high- and low-impact psychology journals. In a second study, we established the generality of reporting errors in a random sample of recent psychological articles. Our results, on the basis of 281 articles, indicate that around 18% of statistical results in the psychological literature are incorrectly reported. Inconsistencies were more common in low-impact journals than in high-impact journals. Moreover, around 15% of the articles contained at least one statistical conclusion that proved, upon recalculation, to be incorrect; that is, recalculation rendered the previously significant result insignificant, or vice versa. These errors were often in line with researchers' expectations. We classified the most common errors and contacted authors to shed light on the origins of the errors.

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

NASA Technical Reports Server (NTRS)

Carder, Kendall L.; Hawes, Steve K.; Lee, Zhongping

1997-01-01

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.

Robust through-the-wall radar image classification using a target-model alignment procedure.

PubMed

Smith, Graeme E; Mobasseri, Bijan G

2012-02-01

A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%. © 2011 IEEE

Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier

NASA Astrophysics Data System (ADS)

Hashemi, H.; Tax, D. M. J.; Duin, R. P. W.; Javaherian, A.; de Groot, P.

2008-11-01

Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

The inverse problem of refraction travel times, part II: Quantifying refraction nonuniqueness using a three-layer model

USGS Publications Warehouse

Ivanov, J.; Miller, R.D.; Xia, J.; Steeples, D.

2005-01-01

This paper is the second of a set of two papers in which we study the inverse refraction problem. The first paper, "Types of Geophysical Nonuniqueness through Minimization," studies and classifies the types of nonuniqueness that exist when solving inverse problems depending on the participation of a priori information required to obtain reliable solutions of inverse geophysical problems. In view of the classification developed, in this paper we study the type of nonuniqueness associated with the inverse refraction problem. An approach for obtaining a realistic solution to the inverse refraction problem is offered in a third paper that is in preparation. The nonuniqueness of the inverse refraction problem is examined by using a simple three-layer model. Like many other inverse geophysical problems, the inverse refraction problem does not have a unique solution. Conventionally, nonuniqueness is considered to be a result of insufficient data and/or error in the data, for any fixed number of model parameters. This study illustrates that even for overdetermined and error free data, nonlinear inverse refraction problems exhibit exact-data nonuniqueness, which further complicates the problem of nonuniqueness. By evaluating the nonuniqueness of the inverse refraction problem, this paper targets the improvement of refraction inversion algorithms, and as a result, the achievement of more realistic solutions. The nonuniqueness of the inverse refraction problem is examined initially by using a simple three-layer model. The observations and conclusions of the three-layer model nonuniqueness study are used to evaluate the nonuniqueness of more complicated n-layer models and multi-parameter cell models such as in refraction tomography. For any fixed number of model parameters, the inverse refraction problem exhibits continuous ranges of exact-data nonuniqueness. Such an unfavorable type of nonuniqueness can be uniquely solved only by providing abundant a priori information. Insufficient a priori information during the inversion is the reason why refraction methods often may not produce desired results or even fail. This work also demonstrates that the application of the smoothing constraints, typical when solving ill-posed inverse problems, has a dual and contradictory role when applied to the ill-posed inverse problem of refraction travel times. This observation indicates that smoothing constraints may play such a two-fold role when applied to other inverse problems. Other factors that contribute to inverse-refraction-problem nonuniqueness are also considered, including indeterminacy, statistical data-error distribution, numerical error and instability, finite data, and model parameters. ?? Birkha??user Verlag, Basel, 2005.

Type I and Type II error concerns in fMRI research: re-balancing the scale

PubMed Central

Cunningham, William A.

2009-01-01

Statistical thresholding (i.e. P-values) in fMRI research has become increasingly conservative over the past decade in an attempt to diminish Type I errors (i.e. false alarms) to a level traditionally allowed in behavioral science research. In this article, we examine the unintended negative consequences of this single-minded devotion to Type I errors: increased Type II errors (i.e. missing true effects), a bias toward studying large rather than small effects, a bias toward observing sensory and motor processes rather than complex cognitive and affective processes and deficient meta-analyses. Power analyses indicate that the reductions in acceptable P-values over time are producing dramatic increases in the Type II error rate. Moreover, the push for a mapwide false discovery rate (FDR) of 0.05 is based on the assumption that this is the FDR in most behavioral research; however, this is an inaccurate assessment of the conventions in actual behavioral research. We report simulations demonstrating that combined intensity and cluster size thresholds such as P < 0.005 with a 10 voxel extent produce a desirable balance between Types I and II error rates. This joint threshold produces high but acceptable Type II error rates and produces a FDR that is comparable to the effective FDR in typical behavioral science articles (while a 20 voxel extent threshold produces an actual FDR of 0.05 with relatively common imaging parameters). We recommend a greater focus on replication and meta-analysis rather than emphasizing single studies as the unit of analysis for establishing scientific truth. From this perspective, Type I errors are self-erasing because they will not replicate, thus allowing for more lenient thresholding to avoid Type II errors. PMID:20035017

Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

NASA Astrophysics Data System (ADS)

McClanahan, James Patrick

Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw in-situ (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (>95%) classification or an advanced characterization using this system. It is necessary to have a larger database fore more accurate system learning.

Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

PubMed

Schwantes-An, Tae-Hwi; Sung, Heejong; Sabourin, Jeremy A; Justice, Cristina M; Sorant, Alexa J M; Wilson, Alexander F

2016-01-01

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log 10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non-normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

Dates fruits classification using SVM

NASA Astrophysics Data System (ADS)

Alzu'bi, Reem; Anushya, A.; Hamed, Ebtisam; Al Sha'ar, Eng. Abdelnour; Vincy, B. S. Angela

2018-04-01

In this paper, we used SVM in classifying various types of dates using their images. Dates have interesting different characteristics that can be valuable to distinguish and determine a particular date type. These characteristics include shape, texture, and color. A system that achieves 100% accuracy was built to classify the dates which can be eatable and cannot be eatable. The built system helps the food industry and customer in classifying dates depending on specific quality measures giving best performance with specific type of dates.

Diagnosis of early gastric cancer using narrow band imaging and acetic acid

PubMed Central

Matsuo, Ken; Takedatsu, Hidetoshi; Mukasa, Michita; Sumie, Hiroaki; Yoshida, Hikaru; Watanabe, Yasutomo; Akiba, Jun; Nakahara, Keita; Tsuruta, Osamu; Torimura, Takuji

2015-01-01

AIM: To determine whether the endoscopic findings of depressed-type early gastric cancers (EGCs) could precisely predict the histological type. METHODS: Ninety depressed-type EGCs in 72 patients were macroscopically and histologically identified. We evaluated the microvascular (MV) and mucosal surface (MS) patterns of depressed-type EGCs using magnifying endoscopy (ME) with narrow-band imaging (NBI) (NBI-ME) and ME enhanced by 1.5% acetic acid, respectively. First, depressed-type EGCs were classified according to MV pattern by NBI-ME. Subsequently, EGCs unclassified by MV pattern were classified according to MS pattern by enhanced ME (EME) images obtained from the same angle. RESULTS: We classified the depressed-type EGCs into the following 2 MV patterns using NBI-ME: a fine-network pattern that indicated differentiated adenocarcinoma (25/25, 100%) and a corkscrew pattern that likely indicated undifferentiated adenocarcinoma (18/23, 78.3%). However, 42 of the 90 (46.7%) lesions could not be classified into MV patterns by NBI-ME. These unclassified lesions were then evaluated for MS patterns using EME, which classified 33 (81.0%) lesions as MS patterns, diagnosed as differentiated adenocarcinoma. As a result, 76 of the 90 (84.4%) lesions were matched with histological diagnoses using a combination of NBI-ME and EME. CONCLUSION: A combination of NBI-ME and EME was useful in predicting the histological type of depressed-type EGC. PMID:25632201

The Relation Between Inflation in Type-I and Type-II Error Rate and Population Divergence in Genome-Wide Association Analysis of Multi-Ethnic Populations.

PubMed

Derks, E M; Zwinderman, A H; Gamazon, E R

2017-05-01

Population divergence impacts the degree of population stratification in Genome Wide Association Studies. We aim to: (i) investigate type-I error rate as a function of population divergence (F ST ) in multi-ethnic (admixed) populations; (ii) evaluate the statistical power and effect size estimates; and (iii) investigate the impact of population stratification on the results of gene-based analyses. Quantitative phenotypes were simulated. Type-I error rate was investigated for Single Nucleotide Polymorphisms (SNPs) with varying levels of F ST between the ancestral European and African populations. Type-II error rate was investigated for a SNP characterized by a high value of F ST . In all tests, genomic MDS components were included to correct for population stratification. Type-I and type-II error rate was adequately controlled in a population that included two distinct ethnic populations but not in admixed samples. Statistical power was reduced in the admixed samples. Gene-based tests showed no residual inflation in type-I error rate.

Medication reconciliation accuracy and patient understanding of intended medication changes on hospital discharge.

PubMed

Ziaeian, Boback; Araujo, Katy L B; Van Ness, Peter H; Horwitz, Leora I

2012-11-01

Adverse drug events after hospital discharge are common and often serious. These events may result from provider errors or patient misunderstanding. To determine the prevalence of medication reconciliation errors and patient misunderstanding of discharge medications. Prospective cohort study Patients over 64 years of age admitted with heart failure, acute coronary syndrome or pneumonia and discharged to home. We assessed medication reconciliation accuracy by comparing admission to discharge medication lists and reviewing charts to resolve discrepancies. Medication reconciliation changes that did not appear intentional were classified as suspected provider errors. We assessed patient understanding of intended medication changes through post-discharge interviews. Understanding was scored as full, partial or absent. We tested the association of relevance of the medication to the primary diagnosis with medication accuracy and with patient understanding, accounting for patient demographics, medical team and primary diagnosis. A total of 377 patients were enrolled in the study. A total of 565/2534 (22.3 %) of admission medications were redosed or stopped at discharge. Of these, 137 (24.2 %) were classified as suspected provider errors. Excluding suspected errors, patients had no understanding of 142/205 (69.3 %) of redosed medications, 182/223 (81.6 %) of stopped medications, and 493 (62.0 %) of new medications. Altogether, 307 patients (81.4 %) either experienced a provider error, or had no understanding of at least one intended medication change. Providers were significantly more likely to make an error on a medication unrelated to the primary diagnosis than on a medication related to the primary diagnosis (odds ratio (OR) 4.56, 95 % confidence interval (CI) 2.65, 7.85, p<0.001). Patients were also significantly more likely to misunderstand medication changes unrelated to the primary diagnosis (OR 2.45, 95 % CI 1.68, 3.55), p<0.001). Medication reconciliation and patient understanding are inadequate in older patients post-discharge. Errors and misunderstandings are particularly common in medications unrelated to the primary diagnosis. Efforts to improve medication reconciliation and patient understanding should not be disease-specific, but should be focused on the whole patient.

Classification

NASA Astrophysics Data System (ADS)

Oza, Nikunj

2012-03-01

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example would represent one sunspot’s classification (y_i) and the corresponding set of measurements (x_i). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the type of sunspot. We may have a test set S—a set of examples not used in training that we use to test how well the model h predicts the outputs on new examples. Just as with the examples in T, the examples in S are assumed to be independent and identically distributed (i.i.d.) draws from the distribution D. We measure the error of h on the test set as the proportion of test cases that h misclassifies: 1/|S| Sigma(x,y union S)[I(h(x)!= y)] where I(v) is the indicator function—it returns 1 if v is true and 0 otherwise. In our sunspot classification example, we would identify additional examples of sunspots that were not used in generating the model, and use these to determine how accurate the model is—the fraction of the test samples that the model classifies correctly. An example of a classification model is the decision tree shown in Figure 23.1. We will discuss the decision tree learning algorithm in more detail later—for now, we assume that, given a training set with examples of sunspots, this decision tree is derived. This can be used to classify previously unseen examples of sunpots. For example, if a new sunspot’s inputs indicate that its "Group Length" is in the range 10-15, then the decision tree would classify the sunspot as being of type “E,” whereas if the "Group Length" is "NULL," the "Magnetic Type" is "bipolar," and the "Penumbra" is "rudimentary," then it would be classified as type "C." In this chapter, we will add to the above description of classification problems. We will discuss decision trees and several other classification models. In particular, we will discuss the learning algorithms that generate these classification models, how to use them to classify new examples, and the strengths and weaknesses of these models. We will end with pointers to further reading on classification methods applied to astronomy data.

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

2010-01-01

Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform better than the NN classifier. The overall best sensitivity, specificity, precision, and accuracy are 92.28%, 98.52%, 53.05%, and 98.41%, respectively.

IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

EPA Science Inventory

Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy.
Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

A Collaborative Assessment Among 11 Pharmaceutical Companies of Misinformation in Commonly Used Online Drug Information Compendia.

PubMed

Randhawa, Amarita S; Babalola, Olakiitan; Henney, Zachary; Miller, Michele; Nelson, Tanya; Oza, Meerat; Patel, Chandni; Randhawa, Anupma S; Riley, Joyce; Snyder, Scott; So, Sherri

2016-05-01

Online drug information compendia (ODIC) are valuable tools that health care professionals (HCPs) and consumers use to educate themselves on pharmaceutical products. Research suggests that these resources, although informative and easily accessible, may contain misinformation, posing risk for product misuse and patient harm. Evaluate drug summaries within ODIC for accuracy and completeness and identify product-specific misinformation. Between August 2014 and January 2015, medical information (MI) specialists from 11 pharmaceutical/biotechnology companies systematically evaluated 270 drug summaries within 5 commonly used ODIC for misinformation. Using a standardized approach, errors were identified; classified as inaccurate, incomplete, or omitted; and categorized per sections of the Full Prescribing Information (FPI). On review of each drug summary, content-correction requests were proposed and supported by the respective product's FPI. Across the 270 drug summaries reviewed within the 5 compendia, the median of the total number of errors identified was 782, with the greatest number of errors occurring in the categories of Dosage and Administration, Patient Education, and Warnings and Precautions. The majority of errors were classified as incomplete, followed by inaccurate and omitted. This analysis demonstrates that ODIC may contain misinformation. HCPs and consumers should be aware of the potential for misinformation and consider more than 1 drug information resource, including the FPI and Medication Guide as well as pharmaceutical/biotechnology companies' MI departments, to obtain unbiased, accurate, and complete product-specific drug information to help support the safe and effective use of prescription drug products. © The Author(s) 2016.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Effects of stress typicality during speeded grammatical classification.

PubMed

Arciuli, Joanne; Cupples, Linda

2003-01-01

The experiments reported here were designed to investigate the influence of stress typicality during speeded grammatical classification of disyllabic English words by native and non-native speakers. Trochaic nouns and iambic gram verbs were considered to be typically stressed, whereas iambic nouns and trochaic verbs were considered to be atypically stressed. Experiments 1a and 2a showed that while native speakers classified typically stressed words individual more quickly and more accurately than atypically stressed words during differences reading, there were no overall effects during classification of spoken stimuli. However, a subgroup of native speakers with high error rates did show a significant effect during classification of spoken stimuli. Experiments 1b and 2b showed that non-native speakers classified typically stressed words more quickly and more accurately than atypically stressed words during reading. Typically stressed words were classified more accurately than atypically stressed words when the stimuli were spoken. Importantly, there was a significant relationship between error rates, vocabulary size and the size of the stress typicality effect in each experiment. We conclude that participants use information about lexical stress to help them distinguish between disyllabic nouns and verbs during speeded grammatical classification. This is especially so for individuals with a limited vocabulary who lack other knowledge (e.g., semantic knowledge) about the differences between these grammatical categories.

Exploring the Phenotype of Phonological Reading Disability as a Function of the Phonological Deficit Severity: Evidence from the Error Analysis Paradigm in Arabic

ERIC Educational Resources Information Center

Taha, Haitham; Ibrahim, Raphiq; Khateb, Asaid

2014-01-01

The dominant error types were investigated as a function of phonological processing (PP) deficit severity in four groups of impaired readers. For this aim, an error analysis paradigm distinguishing between four error types was used. The findings revealed that the different types of impaired readers were characterized by differing predominant error…

Error analysis of mathematical problems on TIMSS: A case of Indonesian secondary students

NASA Astrophysics Data System (ADS)

Priyani, H. A.; Ekawati, R.

2018-01-01

Indonesian students’ competence in solving mathematical problems is still considered as weak. It was pointed out by the results of international assessment such as TIMSS. This might be caused by various types of errors made. Hence, this study aimed at identifying students’ errors in solving mathematical problems in TIMSS in the topic of numbers that considered as the fundamental concept in Mathematics. This study applied descriptive qualitative analysis. The subject was three students with most errors in the test indicators who were taken from 34 students of 8th graders. Data was obtained through paper and pencil test and student’s’ interview. The error analysis indicated that in solving Applying level problem, the type of error that students made was operational errors. In addition, for reasoning level problem, there are three types of errors made such as conceptual errors, operational errors and principal errors. Meanwhile, analysis of the causes of students’ errors showed that students did not comprehend the mathematical problems given.

Comparison between geodetic and oceanographic approaches to estimate mean dynamic topography for vertical datum unification: evaluation at Australian tide gauges

NASA Astrophysics Data System (ADS)

Filmer, M. S.; Hughes, C. W.; Woodworth, P. L.; Featherstone, W. E.; Bingham, R. J.

2018-04-01

The direct method of vertical datum unification requires estimates of the ocean's mean dynamic topography (MDT) at tide gauges, which can be sourced from either geodetic or oceanographic approaches. To assess the suitability of different types of MDT for this purpose, we evaluate 13 physics-based numerical ocean models and six MDTs computed from observed geodetic and/or ocean data at 32 tide gauges around the Australian coast. We focus on the viability of numerical ocean models for vertical datum unification, classifying the 13 ocean models used as either independent (do not contain assimilated geodetic data) or non-independent (do contain assimilated geodetic data). We find that the independent and non-independent ocean models deliver similar results. Maximum differences among ocean models and geodetic MDTs reach >150 mm at several Australian tide gauges and are considered anomalous at the 99% confidence level. These differences appear to be of geodetic origin, but without additional independent information, or formal error estimates for each model, some of these errors remain inseparable. Our results imply that some ocean models have standard deviations of differences with other MDTs (using geodetic and/or ocean observations) at Australian tide gauges, and with levelling between some Australian tide gauges, of ˜ ± 50 mm . This indicates that they should be considered as an alternative to geodetic MDTs for the direct unification of vertical datums. They can also be used as diagnostics for errors in geodetic MDT in coastal zones, but the inseparability problem remains, where the error cannot be discriminated between the geoid model or altimeter-derived mean sea surface.

What does "Diversity" Mean for Public Engagement in Science? A New Metric for Innovation Ecosystem Diversity.

PubMed

Özdemir, Vural; Springer, Simon

2018-03-01

Diversity is increasingly at stake in early 21st century. Diversity is often conceptualized across ethnicity, gender, socioeconomic status, sexual preference, and professional credentials, among other categories of difference. These are important and relevant considerations and yet, they are incomplete. Diversity also rests in the way we frame questions long before answers are sought. Such diversity in the framing (epistemology) of scientific and societal questions is important for they influence the types of data, results, and impacts produced by research. Errors in the framing of a research question, whether in technical science or social science, are known as type III errors, as opposed to the better known type I (false positives) and type II errors (false negatives). Kimball defined "error of the third kind" as giving the right answer to the wrong problem. Raiffa described the type III error as correctly solving the wrong problem. Type III errors are upstream or design flaws, often driven by unchecked human values and power, and can adversely impact an entire innovation ecosystem, waste money, time, careers, and precious resources by focusing on the wrong or incorrectly framed question and hypothesis. Decades may pass while technology experts, scientists, social scientists, funding agencies and management consultants continue to tackle questions that suffer from type III errors. We propose a new diversity metric, the Frame Diversity Index (FDI), based on the hitherto neglected diversities in knowledge framing. The FDI would be positively correlated with epistemological diversity and technological democracy, and inversely correlated with prevalence of type III errors in innovation ecosystems, consortia, and knowledge networks. We suggest that the FDI can usefully measure (and prevent) type III error risks in innovation ecosystems, and help broaden the concepts and practices of diversity and inclusion in science, technology, innovation and society.

The study about forming high-precision optical lens minimalized sinuous error structures for designed surface

NASA Astrophysics Data System (ADS)

Katahira, Yu; Fukuta, Masahiko; Katsuki, Masahide; Momochi, Takeshi; Yamamoto, Yoshihiro

2016-09-01

Recently, it has been required to improve qualities of aspherical lenses mounted on camera units. Optical lenses in highvolume production generally are applied with molding process using cemented carbide or Ni-P coated steel, which can be selected from lens material such as glass and plastic. Additionally it can be obtained high quality of the cut or ground surface on mold due to developments of different mold product technologies. As results, it can be less than 100nmPV as form-error and 1nmRa as surface roughness in molds. Furthermore it comes to need higher quality, not only formerror( PV) and surface roughness(Ra) but also other surface characteristics. For instance, it can be caused distorted shapes at imaging by middle spatial frequency undulations on the lens surface. In this study, we made focus on several types of sinuous structures, which can be classified into form errors for designed surface and deteriorate optical system performances. And it was obtained mold product processes minimalizing undulations on the surface. In the report, it was mentioned about the analyzing process by using PSD so as to evaluate micro undulations on the machined surface quantitatively. In addition, it was mentioned that the grinding process with circumferential velocity control was effective for large aperture lenses fabrication and could minimalize undulations appeared on outer area of the machined surface, and mentioned about the optical glass lens molding process by using the high precision press machine.

Real-time detection of faecally contaminated drinking water with tryptophan-like fluorescence: defining threshold values.

PubMed

Sorensen, James P R; Baker, Andy; Cumberland, Susan A; Lapworth, Dan J; MacDonald, Alan M; Pedley, Steve; Taylor, Richard G; Ward, Jade S T

2018-05-01

We assess the use of fluorescent dissolved organic matter at excitation-emission wavelengths of 280nm and 360nm, termed tryptophan-like fluorescence (TLF), as an indicator of faecally contaminated drinking water. A significant logistic regression model was developed using TLF as a predictor of thermotolerant coliforms (TTCs) using data from groundwater- and surface water-derived drinking water sources in India, Malawi, South Africa and Zambia. A TLF threshold of 1.3ppb dissolved tryptophan was selected to classify TTC contamination. Validation of the TLF threshold indicated a false-negative error rate of 15% and a false-positive error rate of 18%. The threshold was unsuccessful at classifying contaminated sources containing <10 TTC cfu per 100mL, which we consider the current limit of detection. If only sources above this limit were classified, the false-negative error rate was very low at 4%. TLF intensity was very strongly correlated with TTC concentration (ρ s =0.80). A higher threshold of 6.9ppb dissolved tryptophan is proposed to indicate heavily contaminated sources (≥100 TTC cfu per 100mL). Current commercially available fluorimeters are easy-to-use, suitable for use online and in remote environments, require neither reagents nor consumables, and crucially provide an instantaneous reading. TLF measurements are not appreciably impaired by common intereferents, such as pH, turbidity and temperature, within typical natural ranges. The technology is a viable option for the real-time screening of faecally contaminated drinking water globally. Copyright © 2017 Natural Environment Research Council (NERC), as represented by the British Geological Survey (BGS. Published by Elsevier B.V. All rights reserved.

Simplified moment tensor analysis and unified decomposition of acoustic emission source: Application to in situ hydrofracturing test

NASA Astrophysics Data System (ADS)

Ohtsu, Masayasu

1991-04-01

An application of a moment tensor analysis to acoustic emission (AE) is studied to elucidate crack types and orientations of AE sources. In the analysis, simplified treatment is desirable, because hundreds of AE records are obtained from just one experiment and thus sophisticated treatment is realistically cumbersome. Consequently, a moment tensor inversion based on P wave amplitude is employed to determine six independent tensor components. Selecting only P wave portion from the full-space Green's function of homogeneous and isotropic material, a computer code named SiGMA (simplified Green's functions for the moment tensor analysis) is developed for the AE inversion analysis. To classify crack type and to determine crack orientation from moment tensor components, a unified decomposition of eigenvalues into a double-couple (DC) part, a compensated linear vector dipole (CLVD) part, and an isotropic part is proposed. The aim of the decomposition is to determine the proportion of shear contribution (DC) and tensile contribution (CLVD + isotropic) on AE sources and to classify cracks into a crack type of the dominant motion. Crack orientations determined from eigenvectors are presented as crack-opening vectors for tensile cracks and fault motion vectors for shear cracks, instead of stereonets. The SiGMA inversion and the unified decomposition are applied to synthetic data and AE waveforms detected during an in situ hydrofracturing test. To check the accuracy of the procedure, numerical experiments are performed on the synthetic waveforms, including cases with 10% random noise added. Results show reasonable agreement with assumed crack configurations. Although the maximum error is approximately 10% with respect to the ratios, the differences on crack orientations are less than 7°. AE waveforms detected by eight accelerometers deployed during the hydrofracturing test are analyzed. Crack types and orientations determined are in reasonable agreement with a predicted failure plane from borehole TV observation. The results suggest that tensile cracks are generated first at weak seams and then shear cracks follow on the opened joints.

Quantitative evaluation of patient-specific quality assurance using online dosimetry system

NASA Astrophysics Data System (ADS)

Jung, Jae-Yong; Shin, Young-Ju; Sohn, Seung-Chang; Min, Jung-Whan; Kim, Yon-Lae; Kim, Dong-Su; Choe, Bo-Young; Suh, Tae-Suk

2018-01-01

In this study, we investigated the clinical performance of an online dosimetry system (Mobius FX system, MFX) by 1) dosimetric plan verification using gamma passing rates and dose volume metrics and 2) error-detection capability evaluation by deliberately introduced machine error. Eighteen volumetric modulated arc therapy (VMAT) plans were studied. To evaluate the clinical performance of the MFX, we used gamma analysis and dose volume histogram (DVH) analysis. In addition, to evaluate the error-detection capability, we used gamma analysis and DVH analysis utilizing three types of deliberately introduced errors (Type 1: gantry angle-independent multi-leaf collimator (MLC) error, Type 2: gantry angle-dependent MLC error, and Type 3: gantry angle error). A dosimetric verification comparison of physical dosimetry system (Delt4PT) and online dosimetry system (MFX), gamma passing rates of the two dosimetry systems showed very good agreement with treatment planning system (TPS) calculation. For the average dose difference between the TPS calculation and the MFX measurement, most of the dose metrics showed good agreement within a tolerance of 3%. For the error-detection comparison of Delta4PT and MFX, the gamma passing rates of the two dosimetry systems did not meet the 90% acceptance criterion with the magnitude of error exceeding 2 mm and 1.5 ◦, respectively, for error plans of Types 1, 2, and 3. For delivery with all error types, the average dose difference of PTV due to error magnitude showed good agreement between calculated TPS and measured MFX within 1%. Overall, the results of the online dosimetry system showed very good agreement with those of the physical dosimetry system. Our results suggest that a log file-based online dosimetry system is a very suitable verification tool for accurate and efficient clinical routines for patient-specific quality assurance (QA).

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

PubMed Central

2012-01-01

Background Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts. Methods With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated. Results With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms. Conclusions The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance. PMID:23036049

An observational study of drug administration errors in a Malaysian hospital (study of drug administration errors).

PubMed

Chua, S S; Tea, M H; Rahman, M H A

2009-04-01

Drug administration errors were the second most frequent type of medication errors, after prescribing errors but the latter were often intercepted hence, administration errors were more probably to reach the patients. Therefore, this study was conducted to determine the frequency and types of drug administration errors in a Malaysian hospital ward. This is a prospective study that involved direct, undisguised observations of drug administrations in a hospital ward. A researcher was stationed in the ward under study for 15 days to observe all drug administrations which were recorded in a data collection form and then compared with the drugs prescribed for the patient. A total of 1118 opportunities for errors were observed and 127 administrations had errors. This gave an error rate of 11.4 % [95% confidence interval (CI) 9.5-13.3]. If incorrect time errors were excluded, the error rate reduced to 8.7% (95% CI 7.1-10.4). The most common types of drug administration errors were incorrect time (25.2%), followed by incorrect technique of administration (16.3%) and unauthorized drug errors (14.1%). In terms of clinical significance, 10.4% of the administration errors were considered as potentially life-threatening. Intravenous routes were more likely to be associated with an administration error than oral routes (21.3% vs. 7.9%, P < 0.001). The study indicates that the frequency of drug administration errors in developing countries such as Malaysia is similar to that in the developed countries. Incorrect time errors were also the most common type of drug administration errors. A non-punitive system of reporting medication errors should be established to encourage more information to be documented so that risk management protocol could be developed and implemented.

Feasibility Study of ASTER SWIR data prediction

NASA Astrophysics Data System (ADS)

Yamamoto, H.; Gonzalez, L.

2017-12-01

Observation by ASTER SWIR spectral bands are unavailable since 2008 due to anomalously high SWIR detector temperatures, but ASTER VNIR and TIR spectral bands are still valid. SWIR wavelength region is however very useful to determining the land cover or discriminating rock types, etc. In this work, we present the results of a feasibility study for the prediction of ASTER SWIR bands with artificial neural networks (ANN) using ASTER valid bands. The latter are selected over three types of ground data sets, representing desert, rocky and vegetated areas. The ASTER VNIR bands are atmospherically corrected, using the US standard 62 model, without aerosol correction. To optimize the training of the ANN, it is crucial to categorize the input data. In this goal, we have built a histogram using a simple linear combination of the 3 VNIR bands, that we call contrast histogram, to split the input ASTER data in 4 areas. For each of these 4 areas, we have built six ANN, one for each SWIR band to retrieve with 3 inputs and two layers with 5 hidden nodes each and one outputs layer. The training of the ANN is done using ASTER pixels selected over several millions of pixels in representative desert, green and rocky areas. The analysis of the ANN results demonstrates that 99 % of the pixels are reconstructed with less than 20% error in desert areas. In rocky areas, the errors do not exceed 30%. However, the errors can exceed 50% in vegetated areas. This led us to improve the ANN by introducing new spectral bands (1.24, 1.64, 2.13 μm) from TERRA MODIS that is time synchronized with ASTER. The measurements are pan-sharpened to match ASTER spatial resolution. Instead of using a contrast histogram, a NDVI histogram helps us to classify the input data. With the newly constructed ANNs, the quality of the retrieved SWIR values is perceptible in particular over vegetation ( 45% of the points with less than 20% errors), and even more over the desert and rocky areas ( 75% of the points with less than 10% errors). We demonstrate that it is possible to build ANNs that are capable of regenerating, with a reasonable error, the SWIR bands in deserts and mountainous, while SWIR reconstruction in vegetation areas is more difficult. Improvements can be envisaged by introducing missing elements such as snow or ice along with a better discrimination of the vegetation.

Semantic error patterns on the Boston Naming Test in normal aging, amnestic mild cognitive impairment, and mild Alzheimer's disease: is there semantic disruption?

PubMed

Balthazar, Marcio Luiz Figueredo; Cendes, Fernando; Damasceno, Benito Pereira

2008-11-01

Naming difficulty is common in Alzheimer's disease (AD), but the nature of this problem is not well established. The authors investigated the presence of semantic breakdown and the pattern of general and semantic errors in patients with mild AD, patients with amnestic mild cognitive impairment (aMCI), and normal controls by examining their spontaneous answers on the Boston Naming Test (BNT) and verifying whether they needed or were benefited by semantic and phonemic cues. The errors in spontaneous answers were classified in four mutually exclusive categories (semantic errors, visual paragnosia, phonological errors, and omission errors), and the semantic errors were further subclassified as coordinate, superordinate, and circumlocutory. Patients with aMCI performed normally on the BNT and needed fewer semantic and phonemic cues than patients with mild AD. After semantic cues, subjects with aMCI and control subjects gave more correct answers than patients with mild AD, but after phonemic cues, there was no difference between the three groups, suggesting that the low performance of patients with AD cannot be completely explained by semantic breakdown. Patterns of spontaneous naming errors and subtypes of semantic errors were similar in the three groups, with decreasing error frequency from coordinate to superordinate to circumlocutory subtypes.

Error-related brain activity and error awareness in an error classification paradigm.

PubMed

Di Gregorio, Francesco; Steinhauser, Marco; Maier, Martin E

2016-10-01

Error-related brain activity has been linked to error detection enabling adaptive behavioral adjustments. However, it is still unclear which role error awareness plays in this process. Here, we show that the error-related negativity (Ne/ERN), an event-related potential reflecting early error monitoring, is dissociable from the degree of error awareness. Participants responded to a target while ignoring two different incongruent distractors. After responding, they indicated whether they had committed an error, and if so, whether they had responded to one or to the other distractor. This error classification paradigm allowed distinguishing partially aware errors, (i.e., errors that were noticed but misclassified) and fully aware errors (i.e., errors that were correctly classified). The Ne/ERN was larger for partially aware errors than for fully aware errors. Whereas this speaks against the idea that the Ne/ERN foreshadows the degree of error awareness, it confirms the prediction of a computational model, which relates the Ne/ERN to post-response conflict. This model predicts that stronger distractor processing - a prerequisite of error classification in our paradigm - leads to lower post-response conflict and thus a smaller Ne/ERN. This implies that the relationship between Ne/ERN and error awareness depends on how error awareness is related to response conflict in a specific task. Our results further indicate that the Ne/ERN but not the degree of error awareness determines adaptive performance adjustments. Taken together, we conclude that the Ne/ERN is dissociable from error awareness and foreshadows adaptive performance adjustments. Our results suggest that the relationship between the Ne/ERN and error awareness is correlative and mediated by response conflict. Copyright © 2016 Elsevier Inc. All rights reserved.

Compensating for the effects of site and equipment variation on delphinid species identification from their echolocation clicks.

PubMed

Roch, Marie A; Stinner-Sloan, Johanna; Baumann-Pickering, Simone; Wiggins, Sean M

2015-01-01

A concern for applications of machine learning techniques to bioacoustics is whether or not classifiers learn the categories for which they were trained. Unfortunately, information such as characteristics of specific recording equipment or noise environments can also be learned. This question is examined in the context of identifying delphinid species by their echolocation clicks. To reduce the ambiguity between species classification performance and other confounding factors, species whose clicks can be readily distinguished were used in this study: Pacific white-sided and Risso's dolphins. A subset of data from autonomous acoustic recorders located at seven sites in the Southern California Bight collected between 2006 and 2012 was selected. Cepstral-based features were extracted for each echolocation click and Gaussian mixture models were used to classify groups of 100 clicks. One hundred Monte-Carlo three-fold experiments were conducted to examine classification performance where fold composition was determined by acoustic encounter, recorder characteristics, or recording site. The error rate increased from 6.1% when grouped by acoustic encounter to 18.1%, 46.2%, and 33.2% for grouping by equipment, equipment category, and site, respectively. A noise compensation technique reduced error for these grouping schemes to 2.7%, 4.4%, 6.7%, and 11.4%, respectively, a reduction in error rate of 56%-86%.

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

NASA Astrophysics Data System (ADS)

Heleno, Sandra; Matias, Magda; Pina, Pedro; Sousa, António Jorge

2016-04-01

A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

Minimizing calibration time using inter-subject information of single-trial recognition of error potentials in brain-computer interfaces.

PubMed

Iturrate, Iñaki; Montesano, Luis; Chavarriaga, Ricardo; del R Millán, Jose; Minguez, Javier

2011-01-01

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration process limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event-related potentials. Here, we propose the minimization of the calibration time on single-trial error potentials by using classifiers based on inter-subject information. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, and which is able to adapt itself making the calibration phase shorter and transparent to the user.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

NASA Astrophysics Data System (ADS)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Analyzing human errors in flight mission operations

NASA Technical Reports Server (NTRS)

Bruno, Kristin J.; Welz, Linda L.; Barnes, G. Michael; Sherif, Josef

1993-01-01

A long-term program is in progress at JPL to reduce cost and risk of flight mission operations through a defect prevention/error management program. The main thrust of this program is to create an environment in which the performance of the total system, both the human operator and the computer system, is optimized. To this end, 1580 Incident Surprise Anomaly reports (ISA's) from 1977-1991 were analyzed from the Voyager and Magellan projects. A Pareto analysis revealed that 38 percent of the errors were classified as human errors. A preliminary cluster analysis based on the Magellan human errors (204 ISA's) is presented here. The resulting clusters described the underlying relationships among the ISA's. Initial models of human error in flight mission operations are presented. Next, the Voyager ISA's will be scored and included in the analysis. Eventually, these relationships will be used to derive a theoretically motivated and empirically validated model of human error in flight mission operations. Ultimately, this analysis will be used to make continuous process improvements continuous process improvements to end-user applications and training requirements. This Total Quality Management approach will enable the management and prevention of errors in the future.

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1997-01-01

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

Human cell structure-driven model construction for predicting protein subcellular location from biological images.

PubMed

Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

2016-01-01

The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Deep learning classifier with optical coherence tomography images for early dental caries detection

NASA Astrophysics Data System (ADS)

Karimian, Nima; Salehi, Hassan S.; Mahdian, Mina; Alnajjar, Hisham; Tadinada, Aditya

2018-02-01

Dental caries is a microbial disease that results in localized dissolution of the mineral content of dental tissue. Despite considerable decline in the incidence of dental caries, it remains a major health problem in many societies. Early detection of incipient lesions at initial stages of demineralization can result in the implementation of non-surgical preventive approaches to reverse the demineralization process. In this paper, we present a novel approach combining deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for classification of human oral tissues to detect early dental caries. OCT images of oral tissues with various densities were input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets. The initial CNN layer parameters were randomly selected. The training set is split into minibatches, with 10 OCT images per batch. Given a batch of training patches, the CNN employs two convolutional and pooling layers to extract features and then classify each patch based on the probabilities from the SoftMax classification layer (output-layer). Afterward, the CNN calculates the error between the classification result and the reference label, and then utilizes the backpropagation process to fine-tune all the layer parameters to minimize this error using batch gradient descent algorithm. We validated our proposed technique on ex-vivo OCT images of human oral tissues (enamel, cortical-bone, trabecular-bone, muscular-tissue, and fatty-tissue), which attested to effectiveness of our proposed method.

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images.

PubMed

Wu, Allison Chia-Yi; Rifkin, Scott A

2015-03-27

Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle. We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments. The software classifies spots in these images well, with >95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification.

Brain fingerprinting classification concealed information test detects US Navy military medical information with P300

PubMed Central

Farwell, Lawrence A.; Richardson, Drew C.; Richardson, Graham M.; Furedy, John J.

2014-01-01

A classification concealed information test (CIT) used the “brain fingerprinting” method of applying P300 event-related potential (ERP) in detecting information that is (1) acquired in real life and (2) unique to US Navy experts in military medicine. Military medicine experts and non-experts were asked to push buttons in response to three types of text stimuli. Targets contain known information relevant to military medicine, are identified to subjects as relevant, and require pushing one button. Subjects are told to push another button to all other stimuli. Probes contain concealed information relevant to military medicine, and are not identified to subjects. Irrelevants contain equally plausible, but incorrect/irrelevant information. Error rate was 0%. Median and mean statistical confidences for individual determinations were 99.9% with no indeterminates (results lacking sufficiently high statistical confidence to be classified). We compared error rate and statistical confidence for determinations of both information present and information absent produced by classification CIT (Is a probe ERP more similar to a target or to an irrelevant ERP?) vs. comparison CIT (Does a probe produce a larger ERP than an irrelevant?) using P300 plus the late negative component (LNP; together, P300-MERMER). Comparison CIT produced a significantly higher error rate (20%) and lower statistical confidences: mean 67%; information-absent mean was 28.9%, less than chance (50%). We compared analysis using P300 alone with the P300 + LNP. P300 alone produced the same 0% error rate but significantly lower statistical confidences. These findings add to the evidence that the brain fingerprinting methods as described here provide sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence in a CIT on information obtained in the course of real life that is characteristic of individuals with specific training, expertise, or organizational affiliation. PMID:25565941

Error Analysis of Indonesian Junior High School Student in Solving Space and Shape Content PISA Problem Using Newman Procedure

NASA Astrophysics Data System (ADS)

Sumule, U.; Amin, S. M.; Fuad, Y.

2018-01-01

This study aims to determine the types and causes of errors, as well as efforts being attempted to overcome the mistakes made by junior high school students in completing PISA content space and shape. Two subjects were selected based on the mathematical ability test results with the most error, yet they are able to communicate orally and in writing. Two selected subjects then worked on the PISA ability test question and the subjects were interviewed to find out the type and cause of the error and then given a scaffolding based on the type of mistake made.The results of this study obtained the type of error that students do are comprehension and transformation error. The reasons are students was not able to identify the keywords in the question, write down what is known or given, specify formulas or device a plan. To overcome this error, students were given scaffolding. Scaffolding that given to overcome misunderstandings were reviewing and restructuring. While to overcome the transformation error, scaffolding given were reviewing, restructuring, explaining and developing representational tools. Teachers are advised to use scaffolding to resolve errors so that the students are able to avoid these errors.

Bayesian Redshift Classification of Emission-line Galaxies with Photometric Equivalent Widths

NASA Astrophysics Data System (ADS)

Leung, Andrew S.; Acquaviva, Viviana; Gawiser, Eric; Ciardullo, Robin; Komatsu, Eiichiro; Malz, A. I.; Zeimann, Gregory R.; Bridge, Joanna S.; Drory, Niv; Feldmeier, John J.; Finkelstein, Steven L.; Gebhardt, Karl; Gronwall, Caryl; Hagen, Alex; Hill, Gary J.; Schneider, Donald P.

2017-07-01

We present a Bayesian approach to the redshift classification of emission-line galaxies when only a single emission line is detected spectroscopically. We consider the case of surveys for high-redshift Lyα-emitting galaxies (LAEs), which have traditionally been classified via an inferred rest-frame equivalent width (EW {W}{Lyα }) greater than 20 Å. Our Bayesian method relies on known prior probabilities in measured emission-line luminosity functions and EW distributions for the galaxy populations, and returns the probability that an object in question is an LAE given the characteristics observed. This approach will be directly relevant for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), which seeks to classify ˜106 emission-line galaxies into LAEs and low-redshift [{{O}} {{II}}] emitters. For a simulated HETDEX catalog with realistic measurement noise, our Bayesian method recovers 86% of LAEs missed by the traditional {W}{Lyα } > 20 Å cutoff over 2 < z < 3, outperforming the EW cut in both contamination and incompleteness. This is due to the method’s ability to trade off between the two types of binary classification error by adjusting the stringency of the probability requirement for classifying an observed object as an LAE. In our simulations of HETDEX, this method reduces the uncertainty in cosmological distance measurements by 14% with respect to the EW cut, equivalent to recovering 29% more cosmological information. Rather than using binary object labels, this method enables the use of classification probabilities in large-scale structure analyses. It can be applied to narrowband emission-line surveys as well as upcoming large spectroscopic surveys including Euclid and WFIRST.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses.

PubMed

McMullin, Brian T; Leung, Ming-Ying; Shanbhag, Arun S; McNulty, Donald; Mabrey, Jay D; Agrawal, C Mauli

2006-02-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey-Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (p<0.05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses

PubMed Central

McMullin, Brian T.; Leung, Ming-Ying; Shanbhag, Arun S.; McNulty, Donald; Mabrey, Jay D.; Agrawal, C. Mauli

2014-01-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey–Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (po0:05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions. PMID:16112725

Data Mining on Numeric Error in Computerized Physician Order Entry System Prescriptions.

PubMed

Wu, Xue; Wu, Changxu

2017-01-01

This study revealed the numeric error patterns related to dosage when doctors prescribed in computerized physician order entry system. Error categories showed that the '6','7', and '9' key produced a higher incidence of errors in Numpad typing, while the '2','3', and '0' key produced a higher incidence of errors in main keyboard digit line typing. Errors categorized as omission and substitution were higher in prevalence than transposition and intrusion.

Application of Snyder-Dolan Classification Scheme to the Selection of “Orthogonal” Columns for Fast Screening for Illicit Drugs and Impurity Profiling of Pharmaceuticals - I. Isocratic Elution

PubMed Central

Fan, Wenzhe; Zhang, Yu; Carr, Peter W.; Rutan, Sarah C.; Dumarey, Melanie; Schellinger, Adam P.; Pritts, Wayne

2011-01-01

Fourteen judiciously selected reversed-phase columns were tested with 18 cationic drug solutes under the isocratic elution conditions advised in the Snyder-Dolan (S-D) hydrophobic subtraction method of column classification. The standard errors (S.E.) of the least squares regressions of log k′ vs. log k′REF were obtained for a given column against a reference column and used to compare and classify columns based on their selectivity. The results are consistent with those obtained with a study of the 16 test solutes recommended by Snyder and Dolan. To the extent that these drugs are representative these results show that the S-D classification scheme is also generally applicable to pharmaceuticals under isocratic conditions. That is, those columns judged to be similar based on the S-D 16 solutes were similar based on the 18 drugs; furthermore those columns judged to have significantly different selectivities based on the 16 S-D probes appeared to be quite different for the drugs as well. Given that the S-D method has been used to classify more than 400 different types of reversed phases the extension to cationic drugs is a significant finding. PMID:19698948

Algorithms for the detection of chewing behavior in dietary monitoring applications

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.; Helal, Abdelsalam; Mendez-Vasquez, Andres

2009-08-01

The detection of food consumption is key to the implementation of successful behavior modification in support of dietary monitoring and therapy, for example, during the course of controlling obesity, diabetes, or cardiovascular disease. Since the vast majority of humans consume food via mastication (chewing), we have designed an algorithm that automatically detects chewing behaviors in surveillance video of a person eating. Our algorithm first detects the mouth region, then computes the spatiotemporal frequency spectrum of a small perioral region (including the mouth). Spectral data are analyzed to determine the presence of periodic motion that characterizes chewing. A classifier is then applied to discriminate different types of chewing behaviors. Our algorithm was tested on seven volunteers, whose behaviors included chewing with mouth open, chewing with mouth closed, talking, static face presentation (control case), and moving face presentation. Early test results show that the chewing behaviors induce a temporal frequency peak at 0.5Hz to 2.5Hz, which is readily detected using a distance-based classifier. Computational cost is analyzed for implementation on embedded processing nodes, for example, in a healthcare sensor network. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm, and its estimated error. It is shown that chewing detection is possible within a computationally efficient, accurate, and subject-independent framework.

Multiclass Bayes error estimation by a feature space sampling technique

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.

1979-01-01

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Overview of ATMT and Analysis of Subphase IIB

DTIC Science & Technology

1977-12-01

relationships among maximum gunner error and target, vehicle motion. 1 INC LAS SI I F D I II TechidTfa-F ’•-RpoiTLTR-:7- 77 becemboer 1977 Directorate of...of- si ght interruptions, s ijrii i cant. difficulties weri etrcuo (t’red early in the effort to dioitize the anal uj iunnonr error dtta from thi s...and is classified SECRET. h. Phone I si •Jiu, ry. (I) The purpose of the Pha,ne I effort was to identify an array of co(.ddidate Lafneuvern ho b u.,ed

Strategic Use of Microscrews for Enhancing the Accuracy of Computer-Guided Implant Surgery in Fully Edentulous Arches: A Case History Report.

PubMed

Lee, Du-Hyeong

Implant guide systems can be classified by their supporting structure as tooth-, mucosa-, or bone-supported. Mucosa-supported guides for fully edentulous arches show lower accuracy in implant placement because of errors in image registration and guide positioning. This article introduces the application of a novel microscrew system for computer-aided implant surgery. This technique can markedly improve the accuracy of computer-guided implant surgery in fully edentulous arches by eliminating errors from image fusion and guide positioning.

42 CFR 431.960 - Types of payment errors.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Estimating Improper Payments in Medicaid and CHIP § 431.960 Types of payment errors. (a) General rule. State or provider errors identified for the Medicaid and CHIP improper payments measurement under the... been paid by a third party but were inappropriately paid by Medicaid or CHIP. (v) Pricing errors. (vi...

42 CFR 431.960 - Types of payment errors.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Estimating Improper Payments in Medicaid and CHIP § 431.960 Types of payment errors. (a) General rule. State or provider errors identified for the Medicaid and CHIP improper payments measurement under the... been paid by a third party but were inappropriately paid by Medicaid or CHIP. (v) Pricing errors. (vi...

42 CFR 431.960 - Types of payment errors.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Estimating Improper Payments in Medicaid and CHIP § 431.960 Types of payment errors. (a) General rule. State or provider errors identified for the Medicaid and CHIP improper payments measurement under the... been paid by a third party but were inappropriately paid by Medicaid or CHIP. (v) Pricing errors. (vi...

42 CFR 431.960 - Types of payment errors.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Estimating Improper Payments in Medicaid and CHIP § 431.960 Types of payment errors. (a) General rule. State or provider errors identified for the Medicaid and CHIP improper payments measurement under the... been paid by a third party but were inappropriately paid by Medicaid or CHIP. (v) Pricing errors. (vi...

Attitude errors arising from antenna/satellite altitude errors - Recognition and reduction

NASA Technical Reports Server (NTRS)

Godbey, T. W.; Lambert, R.; Milano, G.

1972-01-01

A review is presented of the three basic types of pulsed radar altimeter designs, as well as the source and form of altitude bias errors arising from antenna/satellite attitude errors in each design type. A quantitative comparison of the three systems was also made.

Inferring Planet Occurrence Rates With a Q1-Q17 Kepler Planet Candidate Catalog Produced by a Machine Learning Classifier

NASA Astrophysics Data System (ADS)

Catanzarite, Joseph; Jenkins, Jon Michael; McCauliff, Sean D.; Burke, Christopher; Bryson, Steve; Batalha, Natalie; Coughlin, Jeffrey; Rowe, Jason; mullally, fergal; thompson, susan; Seader, Shawn; Twicken, Joseph; Li, Jie; morris, robert; smith, jeffrey; haas, michael; christiansen, jessie; Clarke, Bruce

2015-08-01

NASA’s Kepler Space Telescope monitored the photometric variations of over 170,000 stars, at half-hour cadence, over its four-year prime mission. The Kepler pipeline calibrates the pixels of the target apertures for each star, produces light curves with simple aperture photometry, corrects for systematic error, and detects threshold-crossing events (TCEs) that may be due to transiting planets. The pipeline estimates planet parameters for all TCEs and computes diagnostics used by the Threshold Crossing Event Review Team (TCERT) to produce a catalog of objects that are deemed either likely transiting planet candidates or false positives.We created a training set from the Q1-Q12 and Q1-Q16 TCERT catalogs and an ensemble of synthetic transiting planets that were injected at the pixel level into all 17 quarters of data, and used it to train a random forest classifier. The classifier uniformly and consistently applies diagnostics developed by the Transiting Planet Search and Data Validation pipeline components and by TCERT to produce a robust catalog of planet candidates.The characteristics of the planet candidates detected by Kepler (planet radius and period) do not reflect the intrinsic planet population. Detection efficiency is a function of SNR, so the set of detected planet candidates is incomplete. Transit detection preferentially finds close-in planets with nearly edge-on orbits and misses planets whose orbital geometry precludes transits. Reliability of the planet candidates must also be considered, as they may be false positives. Errors in detected planet radius and in assumed star properties can also bias inference of intrinsic planet population characteristics.In this work we infer the intrinsic planet population, starting with the catalog of detected planet candidates produced by our random forest classifier, and accounting for detection biases and reliabilities as well as for radius errors in the detected population.Kepler was selected as the 10th mission of the Discovery Program. Funding for this mission is provided by NASA, Science Mission Directorate.

Slow Learner Errors Analysis in Solving Fractions Problems in Inclusive Junior High School Class

NASA Astrophysics Data System (ADS)

Novitasari, N.; Lukito, A.; Ekawati, R.

2018-01-01

A slow learner whose IQ is between 71 and 89 will have difficulties in solving mathematics problems that often lead to errors. The errors could be analyzed to where the errors may occur and its type. This research is qualitative descriptive which aims to describe the locations, types, and causes of slow learner errors in the inclusive junior high school class in solving the fraction problem. The subject of this research is one slow learner of seventh-grade student which was selected through direct observation by the researcher and through discussion with mathematics teacher and special tutor which handles the slow learner students. Data collection methods used in this study are written tasks and semistructured interviews. The collected data was analyzed by Newman’s Error Analysis (NEA). Results show that there are four locations of errors, namely comprehension, transformation, process skills, and encoding errors. There are four types of errors, such as concept, principle, algorithm, and counting errors. The results of this error analysis will help teachers to identify the causes of the errors made by the slow learner.

Quality Issues in Propulsion

NASA Technical Reports Server (NTRS)

McCarty, John P.; Lyles, Garry M.

1997-01-01

Propulsion system quality is defined in this paper as having high reliability, that is, quality is a high probability of within-tolerance performance or operation. Since failures are out-of-tolerance performance, the probability of failures and their occurrence is the difference between high and low quality systems. Failures can be described at 3 levels: the system failure (which is the detectable end of a failure), the failure mode (which is the failure process), and the failure cause (which is the start). Failure causes can be evaluated & classified by type. The results of typing flight history failures shows that most failures are in unrecognized modes and result from human error or noise, i.e. failures are when engineers learn how things really work. Although the study based on US launch vehicles, a sampling of failures from other countries indicates the finding has broad application. The parameters of the design of a propulsion system are not single valued, but have dispersions associated with the manufacturing of parts. Many tests are needed to find failures, if the dispersions are large relative to tolerances, which could contribute to the large number of failures in unrecognized modes.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Drilling Rig Operation Mode Recognition by an Artificial Neuronet

NASA Astrophysics Data System (ADS)

Abu-Abed, Fares; Borisov, Nikolay

2017-11-01

The article proposes a way to develop a drilling rig operation mode classifier specialized to recognize pre-emergency situations appearable in commercial oil-and-gas well drilling. The classifier is based on the theory of image recognition and artificial neuronet taught on real geological and technological information obtained while drilling. To teach the neuronet, a modified backpropagation algorithm that can teach to reach the global extremum of a target function has been proposed. The target function was a relative recognition error to minimize in the teaching. Two approaches to form the drilling rig pre-emergency situation classifier based on a taught neuronet have been considered. The first one involves forming an output classifier of N different signals, each of which corresponds to a single recognizable situation and, and can be formed on the basis of the analysis of M indications, that is using a uniform indication vocabulary for all recognized situations. The second way implements a universal classifier comprising N specialized ones, each of which can recognize a single pre-emergency situation and having a single output.

Identification of terrain cover using the optimum polarimetric classifier

NASA Technical Reports Server (NTRS)

Kong, J. A.; Swartz, A. A.; Yueh, H. A.; Novak, L. M.; Shin, R. T.

1988-01-01

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow-covered fields is developed using the optimum polarimetric classifier. The covariance matrices for various terrain cover are computed from theoretical models of random medium by evaluating the scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Analytical and Monte Carlo simulated classification errors using the fully polarimetric feature vector are compared with classification based on single features which include the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

Automatic interface measurement and analysis. [shoreline length of Alabama using LANDSAT imagery

NASA Technical Reports Server (NTRS)

Faller, K. H.

1975-01-01

A technique for detecting and measuring the interface between two categories in classified scanner data is described together with two application demonstrations. Measurements were found to be accurate to 1.5% root mean square error on features of known length while comparison of measurements made using the technique on LANDSAT data to opisometer measurements on 1:24,000 scale maps shows excellent agreement. Application of the technique to two frames of LANDSAT data classified using a two channel, two class classifier resulted in a computation of 64 km annual decrease in shoreline length. The tidal shoreline of a portion of Alabama was measured using LANDSAT data. Based on the measurement of this portion, the total tidal shoreline length of Alabama is estimated to be 1313 kilometers.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

[Management of medication errors in general medical practice: Study in a pluriprofessionnal health care center].

PubMed

Pourrain, Laure; Serin, Michel; Dautriche, Anne; Jacquetin, Fréderic; Jarny, Christophe; Ballenecker, Isabelle; Bahous, Mickaël; Sgro, Catherine

2018-06-07

Medication errors are the most frequent medical care adverse events in France. Their management process used in hospital remains poorly applied in primary ambulatory care. The main objective of our study was to assess medication error management in general ambulatory practice. The secondary objectives were the characterization of the errors and the analysis of their root causes in order to implement corrective measures. The study was performed in a pluriprofessionnal health care house, applying the stages and tools validated by the French high health authority, that we previously adapted to ambulatory medical cares. During the 3 months study 4712 medical consultations were performed and we collected 64 medication errors. Most of affected patients were at the extreme ages of life (9,4 % before 9 years and 64 % after 70 years). Medication errors occurred at home in 39,1 % of cases, at pluriprofessionnal health care house (25,0 %) or at drugstore (17,2 %). They led to serious clinical consequences (classified as major, critical or catastrophic) in 17,2 % of cases. Drug induced adverse effects occurred in 5 patients, 3 of them needing hospitalization (1 patient recovered, 1 displayed sequelae and 1 died). In more than half of cases, the errors occurred at prescribing stage. The most frequent type of errors was the use of a wrong drug, different from that indicated for the patient (37,5 %) and poor treatment adherence (18,75 %). The systemic reported causes were a care process dysfunction (in coordination or procedure), the health care action context (patient home, not planned act, professional overwork), human factors such as patient and professional condition. The professional team adherence to the study was excellent. Our study demonstrates, for the first time in France, that medication errors management in ambulatory general medical care can be implemented in a pluriprofessionnal health care house with two conditions: the presence of a trained team coordinator, and the use of validated adapted and simple processes and tools. This study also shows that medications errors in general practice are specific of the care process organization. We identified vulnerable points, as transferring and communication between home and care facilities or conversely, medical coordination and involvement of the patient himself in his care. Copyright © 2018 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.

Library Consultants: Client Views.

ERIC Educational Resources Information Center

Robbins-Carter, Jane

1984-01-01

Reviews the consulting process (two-way interaction focused on seeking, giving, and receiving of help) as it applies to library science and identifies nine process roles of the consultant as teacher, student, detective, barbarian, timekeeper, monitor, talisman, advocate, and ritual pig. Common errors in classifying consultant roles are noted. (9…

Didn't You Run the Spell Checker? Effects of Type of Spelling Error and Use of a Spell Checker on Perceptions of the Author

ERIC Educational Resources Information Center

Figueredo, Lauren; Varnhagen, Connie K.

2005-01-01

We investigated expectations regarding a writer's responsibility to proofread text for spelling errors when using a word processor. Undergraduate students read an essay and completed a questionnaire regarding their perceptions of the author and the quality of the essay. They then manipulated type of spelling error (no error, homophone error,…

Trends in Health Information Technology Safety: From Technology-Induced Errors to Current Approaches for Ensuring Technology Safety

PubMed Central

2013-01-01

Objectives Health information technology (HIT) research findings suggested that new healthcare technologies could reduce some types of medical errors while at the same time introducing classes of medical errors (i.e., technology-induced errors). Technology-induced errors have their origins in HIT, and/or HIT contribute to their occurrence. The objective of this paper is to review current trends in the published literature on HIT safety. Methods A review and synthesis of the medical and life sciences literature focusing on the area of technology-induced error was conducted. Results There were four main trends in the literature on technology-induced error. The following areas were addressed in the literature: definitions of technology-induced errors; models, frameworks and evidence for understanding how technology-induced errors occur; a discussion of monitoring; and methods for preventing and learning about technology-induced errors. Conclusions The literature focusing on technology-induced errors continues to grow. Research has focused on the defining what an error is, models and frameworks used to understand these new types of errors, monitoring of such errors and methods that can be used to prevent these errors. More research will be needed to better understand and mitigate these types of errors. PMID:23882411

Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 and Type 2 Diabetes in England.

PubMed

McQueen, Robert Brett; Breton, Marc D; Craig, Joyce; Holmes, Hayden; Whittington, Melanie D; Ott, Markus A; Campbell, Jonathan D

2018-04-01

The objective was to model clinical and economic outcomes of self-monitoring blood glucose (SMBG) devices with varying error ranges and strip prices for type 1 and insulin-treated type 2 diabetes patients in England. We programmed a simulation model that included separate risk and complication estimates by type of diabetes and evidence from in silico modeling validated by the Food and Drug Administration. Changes in SMBG error were associated with changes in hemoglobin A1c (HbA1c) and separately, changes in hypoglycemia. Markov cohort simulation estimated clinical and economic outcomes. A SMBG device with 8.4% error and strip price of £0.30 (exceeding accuracy requirements by International Organization for Standardization [ISO] 15197:2013/EN ISO 15197:2015) was compared to a device with 15% error (accuracy meeting ISO 15197:2013/EN ISO 15197:2015) and price of £0.20. Outcomes were lifetime costs, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). With SMBG errors associated with changes in HbA1c only, the ICER was £3064 per QALY in type 1 diabetes and £264 668 per QALY in insulin-treated type 2 diabetes for an SMBG device with 8.4% versus 15% error. With SMBG errors associated with hypoglycemic events only, the device exceeding accuracy requirements was cost-saving and more effective in insulin-treated type 1 and type 2 diabetes. Investment in devices with higher strip prices but improved accuracy (less error) appears to be an efficient strategy for insulin-treated diabetes patients at high risk of severe hypoglycemia.

TH-CD-202-06: A Method for Characterizing and Validating Dynamic Lung Density Change During Quiet Respiration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dou, T; Ruan, D; Heinrich, M

2016-06-15

Purpose: To obtain a functional relationship that calibrates the lung tissue density change under free breathing conditions through correlating Jacobian values to the Hounsfield units. Methods: Free-breathing lung computed tomography images were acquired using a fast helical CT protocol, where 25 scans were acquired per patient. Using a state-of-the-art deformable registration algorithm, a set of the deformation vector fields (DVF) was generated to provide spatial mapping from the reference image geometry to the other free-breathing scans. These DVFs were used to generate Jacobian maps, which estimate voxelwise volume change. Subsequently, the set of 25 corresponding Jacobian and voxel intensity inmore » Hounsfield units (HU) were collected and linear regression was performed based on the mass conservation relationship to correlate the volume change to density change. Based on the resulting fitting coefficients, the tissues were classified into parenchymal (Type I), vascular (Type II), and soft tissue (Type III) types. These coefficients modeled the voxelwise density variation during quiet breathing. The accuracy of the proposed method was assessed using mean absolute difference in HU between the CT scan intensities and the model predicted values. In addition, validation experiments employing a leave-five-out method were performed to evaluate the model accuracy. Results: The computed mean model errors were 23.30±9.54 HU, 29.31±10.67 HU, and 35.56±20.56 HU, respectively, for regions I, II, and III, respectively. The cross validation experiments averaged over 100 trials had mean errors of 30.02 ± 1.67 HU over the entire lung. These mean values were comparable with the estimated CT image background noise. Conclusion: The reported validation experiment statistics confirmed the lung density modeling during free breathing. The proposed technique was general and could be applied to a wide range of problem scenarios where accurate dynamic lung density information is needed. This work was supported in part by NIH R01 CA0096679.« less

Clinical vision characteristics of the congenital achromatopsias. I. Visual acuity, refractive error, and binocular status.

PubMed

Haegerstrom-Portnoy, G; Schneck, M E; Verdon, W A; Hewlett, S E

1996-07-01

Visual acuity, refractive error, and binocular status were determined in 43 autosomal recessive (AR) and 15 X-linked (XL) congenital achromats. The achromats were classified by color matching and spectral sensitivity data. Large interindividual variation in refractive error and visual acuity was present within each achromat group (complete AR, incomplete AR, and XL). However, the number of individuals with significant interocular acuity differences is very small. Most XLs are myopic; ARs show a wide range of refractive error from high myopia to high hyperopia. Acuity of the AR and XL groups was very similar. With-the-rule astigmatism of large amount is very common in achromats, particularly ARs. There is a close association between strabismus and interocular acuity differences in the ARs, with the fixating eye having better than average acuity. The large overlap of acuity and refractive error of XL and AR achromats suggests that these measures are less useful for differential diagnosis than generally indicated by the clinical literature.

Evaluating segmentation error without ground truth.

PubMed

Kohlberger, Timo; Singh, Vivek; Alvino, Chris; Bahlmann, Claus; Grady, Leo

2012-01-01

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

PubMed

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

A Just Culture Approach to Managing Medication Errors.

PubMed

Rogers, Erin; Griffin, Emily; Carnie, William; Melucci, Joseph; Weber, Robert J

2017-04-01

Medication errors continue to be a concern of health care providers and the public, in particular how to prevent harm from medication mistakes. Many health care workers are afraid to report errors for fear of retribution including the loss of professional licensure and even imprisonment. Most health care workers are silent, instead of admitting their mistake and discussing it openly with peers. This can result in further patient harm if the system causing the mistake is not identified and fixed; thus self-denial may have a negative impact on patient care outcomes. As a result, pharmacy leaders, in collaboration with others, must put systems in place that serve to prevent medication errors while promoting a "Just Culture" way of managing performance and outcomes. This culture must exist across disciplines and departments. Pharmacy leaders need to understand how to classify behaviors associated with errors, set realistic expectations, instill values for staff, and promote accountability within the workplace. This article reviews the concept of Just Culture and provides ways that pharmacy directors can use this concept to manage the degree of error in patient-centered pharmacy services.

Using nurses and office staff to report prescribing errors in primary care.

PubMed

Kennedy, Amanda G; Littenberg, Benjamin; Senders, John W

2008-08-01

To implement a prescribing-error reporting system in primary care offices and analyze the reports. Descriptive analysis of a voluntary prescribing-error-reporting system Seven primary care offices in Vermont, USA. One hundred and three prescribers, managers, nurses and office staff. Nurses and office staff were asked to report all communications with community pharmacists regarding prescription problems. All reports were classified by severity category, setting, error mode, prescription domain and error-producing conditions. All practices submitted reports, although reporting decreased by 3.6 reports per month (95% CI, -2.7 to -4.4, P<0.001, by linear regression analysis). Two hundred and sixteen reports were submitted. Nearly 90% (142/165) of errors were severity Category B (errors that did not reach the patient) according to the National Coordinating Council for Medication Error Reporting and Prevention Index for Categorizing Medication Errors. Nineteen errors reached the patient without causing harm (Category C); and 4 errors caused temporary harm requiring intervention (Category E). Errors involving strength were found in 30% of reports, including 23 prescriptions written for strengths not commercially available. Antidepressants, narcotics and antihypertensives were the most frequent drug classes reported. Participants completed an exit survey with a response rate of 84.5% (87/103). Nearly 90% (77/87) of respondents were willing to continue reporting after the study ended, however none of the participants currently submit reports. Nurses and office staff are a valuable resource for reporting prescribing errors. However, without ongoing reminders, the reporting system is not sustainable.

The Biology of Linguistic Expression Impacts Neural Correlates for Spatial Language

PubMed Central

Emmorey, Karen; McCullough, Stephen; Mehta, Sonya; Ponto, Laura L. B.; Grabowski, Thomas J.

2013-01-01

Biological differences between signed and spoken languages may be most evident in the expression of spatial information. PET was used to investigate the neural substrates supporting the production of spatial language in American Sign Language as expressed by classifier constructions, in which handshape indicates object type and the location/motion of the hand iconically depicts the location/motion of a referent object. Deaf native signers performed a picture description task in which they overtly named objects or produced classifier constructions that varied in location, motion, or object type. In contrast to the expression of location and motion, the production of both lexical signs and object type classifier morphemes engaged left inferior frontal cortex and left inferior temporal cortex, supporting the hypothesis that unlike the location and motion components of a classifier construction, classifier handshapes are categorical morphemes that are retrieved via left hemisphere language regions. In addition, lexical signs engaged the anterior temporal lobes to a greater extent than classifier constructions, which we suggest reflects increased semantic processing required to name individual objects compared with simply indicating the type of object. Both location and motion classifier constructions engaged bilateral superior parietal cortex, with some evidence that the expression of static locations differentially engaged the left intraparietal sulcus. We argue that bilateral parietal activation reflects the biological underpinnings of sign language. To express spatial information, signers must transform visual–spatial representations into a body-centered reference frame and reach toward target locations within signing space. PMID:23249348

Scoliosis curve type classification using kernel machine from 3D trunk image

NASA Astrophysics Data System (ADS)

Adankon, Mathias M.; Dansereau, Jean; Parent, Stefan; Labelle, Hubert; Cheriet, Farida

2012-03-01

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

USGS Publications Warehouse

Singer, D.A.

2006-01-01

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Choosing the Most Effective Pattern Classification Model under Learning-Time Constraint.

PubMed

Saito, Priscila T M; Nakamura, Rodrigo Y M; Amorim, Willian P; Papa, João P; de Rezende, Pedro J; Falcão, Alexandre X

2015-01-01

Nowadays, large datasets are common and demand faster and more effective pattern analysis techniques. However, methodologies to compare classifiers usually do not take into account the learning-time constraints required by applications. This work presents a methodology to compare classifiers with respect to their ability to learn from classification errors on a large learning set, within a given time limit. Faster techniques may acquire more training samples, but only when they are more effective will they achieve higher performance on unseen testing sets. We demonstrate this result using several techniques, multiple datasets, and typical learning-time limits required by applications.

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

PubMed

Yuan, Yuchen; Shi, Yi; Li, Changyang; Kim, Jinman; Cai, Weidong; Han, Zeguang; Feng, David Dagan

2016-12-23

With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance. To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification. Experimental results on our curated TCGA-DeepGene dataset, which is a reformulated subset of the TCGA dataset containing 12 selected types of cancer, show that CGF, ISR and DNN all contribute in improving the overall classification performance. We further compare DeepGene with three widely adopted classifiers and demonstrate that DeepGene has at least 24% performance improvement in terms of testing accuracy. Based on deep learning and somatic point mutation data, we devise DeepGene, an advanced cancer type classifier, which addresses the obstacles in existing SMCC studies. Experiments indicate that DeepGene outperforms three widely adopted existing classifiers, which is mainly attributed to its deep learning module that is able to extract the high level features between combinatorial somatic point mutations and cancer types.

Neuropsychological analysis of a typewriting disturbance following cerebral damage.

PubMed

Boyle, M; Canter, G J

1987-01-01

Following a left CVA, a skilled professional typist sustained a disturbance of typing disproportionate to her handwriting disturbance. Typing errors were predominantly of the sequencing type, with spatial errors much less frequent, suggesting that the impairment was based on a relatively early (premotor) stage of processing. Depriving the subject of visual feedback during handwriting greatly increased her error rate. Similarly, interfering with auditory feedback during speech substantially reduced her self-correction of speech errors. These findings suggested that impaired ability to utilize somesthetic information--probably caused by the subject's parietal lobe lesion--may have been the basis of the typing disorder.

Working memory capacity and task goals modulate error-related ERPs.

PubMed

Coleman, James R; Watson, Jason M; Strayer, David L

2018-03-01

The present study investigated individual differences in information processing following errant behavior. Participants were initially classified as high or as low working memory capacity using the Operation Span Task. In a subsequent session, they then performed a high congruency version of the flanker task under both speed and accuracy stress. We recorded ERPs and behavioral measures of accuracy and response time in the flanker task with a primary focus on processing following an error. The error-related negativity was larger for the high working memory capacity group than for the low working memory capacity group. The positivity following an error (Pe) was modulated to a greater extent by speed-accuracy instruction for the high working memory capacity group than for the low working memory capacity group. These data help to explicate the neural bases of individual differences in working memory capacity and cognitive control. © 2017 Society for Psychophysiological Research.

Assessing explicit error reporting in the narrative electronic medical record using keyword searching.

PubMed

Cao, Hui; Stetson, Peter; Hripcsak, George

2003-01-01

In this study, we assessed the explicit reporting of medical errors in the electronic record. We looked for cases in which the provider explicitly stated that he or she or another provider had committed an error. The advantage of the technique is that it is not limited to a specific type of error. Our goals were to 1) measure the rate at which medical errors were documented in medical records, and 2) characterize the types of errors that were reported.

Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

PubMed

Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki

2014-11-01

Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

The decline and fall of Type II error rates

Treesearch

Steve Verrill; Mark Durst

2005-01-01

For general linear models with normally distributed random errors, the probability of a Type II error decreases exponentially as a function of sample size. This potentially rapid decline reemphasizes the importance of performing power calculations.

Sleep Patterns and Its Relationship to Schooling and Family.

ERIC Educational Resources Information Center

Jones, Franklin Ross

Diagnostic classifications of sleep and arousal disorders have been categorized in four major areas: disorders of initiating and maintaining sleep, disorders of excessive sleepiness, disorders of the sleep/wake pattern, and the parasomnias such as sleep walking, talking, and night errors. Another nomenclature classifies them into DIMS (disorders…

Nephrocalcinosis as adult presentation of Bartter syndrome type II.

PubMed

Huang, L; Luiken, G P M; van Riemsdijk, I C; Petrij, F; Zandbergen, A A M; Dees, A

2014-02-01

Bartter syndrome consists a group of rare autosomal-recessive renal tubulopathies characterised by renal salt wasting, hypokalaemic metabolic alkalosis, hypercalciuria and hyperreninaemic hyperaldosteronism. It is classified into five types. Mutations in the KCNJ1 gene (classified as type II) usually cause the neonatal form of Bartter syndrome. We describe an adult patient with a homozygous KCNJ1 mutation resulting in a remarkably mild phenotype of neonatal type Bartter syndrome.

Nurse practitioner malpractice data: Informing nursing education.

PubMed

Sweeney, Casey Fryer; LeMahieu, Anna; Fryer, George E

Nurse practitioners (NPs) are often identified in medical malpractice claims. However, the use of malpractice data to inform the development of nursing curriculum is limited. The purpose of this study is to examine medical errors committed by NPs. Using National Practitioner Data Bank public use data, years 1990 to 2014, NP malpractice claims were classified by event type, patient outcome, setting, and number of practitioners involved. The greatest proportion of malpractice claims involving nurse practitioners were diagnosis related (41.46%) and treatment related (30.79%). Severe patient outcomes most often occurred in the outpatient setting. Nurse practitioners were independently responsible for the event in the majority of the analyzed claims. Moving forward, nurse practitioner malpractice data should be continuously analyzed and used to inform the development of nurse practitioner education standards and graduate program curriculum to address areas of clinical weakness and improve quality of care and patient safety. Copyright © 2017 Elsevier Inc. All rights reserved.

Restrictions on surgical resident shift length does not impact type of medical errors.

PubMed

Anderson, Jamie E; Goodman, Laura F; Jensen, Guy W; Salcedo, Edgardo S; Galante, Joseph M

2017-05-15

In 2011, resident duty hours were restricted in an attempt to improve patient safety and resident education. With the goal of reducing fatigue, shorter shift length leads to more patient handoffs, raising concerns about adverse effects on patient safety. This study seeks to determine whether differences in duty-hour restrictions influence types of errors made by residents. This is a nested retrospective cohort study at a surgery department in an academic medical center. During 2013-14, standard 2011 duty hours were in place for residents. In 2014-15, duty-hour restrictions at the study site were relaxed ("flexible") with no restrictions on shift length. We reviewed all morbidity and mortality submissions from July 1, 2013-June 30, 2015 and compared differences in types of errors between these periods. A total of 383 patients experienced adverse events, including 59 deaths (15.4%). Comparing standard versus flexible periods, there was no difference in mortality (15.7% versus 12.6%, P = 0.479) or complication rates (2.6% versus 2.5%, P = 0.696). There was no difference in types of errors between periods (P = 0.050-0.808). The most number of errors were due to cognitive failures (229, 59.6%), whereas the fewest number of errors were due to team failure (127, 33.2%). By subset, technical errors resulted in the highest number of errors (169, 44.1%). There were no differences between types of errors of cases that were nonelective, at night, or involving residents. Among adverse events reported in this departmental surgical morbidity and mortality, there were no differences in types of errors when resident duty hours were less restrictive. Copyright © 2017 Elsevier Inc. All rights reserved.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

ASCERTAINMENT OF ON-ROAD SAFETY ERRORS BASED ON VIDEO REVIEW

PubMed Central

Dawson, Jeffrey D.; Uc, Ergun Y.; Anderson, Steven W.; Dastrup, Elizabeth; Johnson, Amy M.; Rizzo, Matthew

2011-01-01

Summary Using an instrumented vehicle, we have studied several aspects of the on-road performance of healthy and diseased elderly drivers. One goal from such studies is to ascertain the type and frequency of driving safety errors. Because the judgment of such errors is somewhat subjective, we applied a taxonomy system of 15 general safety error categories and 76 specific safety error types. We also employed and trained professional driving instructors to review the video data of the on-road drives. In this report, we illustrate our rating system on a group of 111 drivers, ages 65 to 89. These drivers made errors in 13 of the 15 error categories, comprising 42 of the 76 error types. A mean (SD) of 35.8 (12.8) safety errors per drive were noted, with 2.1 (1.7) of them being judged as serious. Our methodology may be useful in applications such as intervention studies, and in longitudinal studies of changes in driving abilities in patients with declining cognitive ability. PMID:24273753

Zero tolerance prescribing: a strategy to reduce prescribing errors on the paediatric intensive care unit.

PubMed

Booth, Rachelle; Sturgess, Emma; Taberner-Stokes, Alison; Peters, Mark

2012-11-01

To establish the baseline prescribing error rate in a tertiary paediatric intensive care unit (PICU) and to determine the impact of a zero tolerance prescribing (ZTP) policy incorporating a dedicated prescribing area and daily feedback of prescribing errors. A prospective, non-blinded, observational study was undertaken in a 12-bed tertiary PICU over a period of 134 weeks. Baseline prescribing error data were collected on weekdays for all patients for a period of 32 weeks, following which the ZTP policy was introduced. Daily error feedback was introduced after a further 12 months. Errors were sub-classified as 'clinical', 'non-clinical' and 'infusion prescription' errors and the effects of interventions considered separately. The baseline combined prescribing error rate was 892 (95 % confidence interval (CI) 765-1,019) errors per 1,000 PICU occupied bed days (OBDs), comprising 25.6 % clinical, 44 % non-clinical and 30.4 % infusion prescription errors. The combined interventions of ZTP plus daily error feedback were associated with a reduction in the combined prescribing error rate to 447 (95 % CI 389-504) errors per 1,000 OBDs (p < 0.0001), an absolute risk reduction of 44.5 % (95 % CI 40.8-48.0 %). Introduction of the ZTP policy was associated with a significant decrease in clinical and infusion prescription errors, while the introduction of daily error feedback was associated with a significant reduction in non-clinical prescribing errors. The combined interventions of ZTP and daily error feedback were associated with a significant reduction in prescribing errors in the PICU, in line with Department of Health requirements of a 40 % reduction within 5 years.

Comparison of artificial intelligence classifiers for SIP attack data

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Slachta, Jiri

2016-05-01

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

Prediction of healthy blood with data mining classification by using Decision Tree, Naive Baysian and SVM approaches

NASA Astrophysics Data System (ADS)

Khalilinezhad, Mahdieh; Minaei, Behrooz; Vernazza, Gianni; Dellepiane, Silvana

2015-03-01

Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naïve Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

Multiplex Microsphere Immunoassays for the Detection of IgM and IgG to Arboviral Diseases

PubMed Central

Basile, Alison J.; Horiuchi, Kalanthe; Panella, Amanda J.; Laven, Janeen; Kosoy, Olga; Lanciotti, Robert S.; Venkateswaran, Neeraja; Biggerstaff, Brad J.

2013-01-01

Serodiagnosis of arthropod-borne viruses (arboviruses) at the Division of Vector-Borne Diseases, CDC, employs a combination of individual enzyme-linked immunosorbent assays and microsphere immunoassays (MIAs) to test for IgM and IgG, followed by confirmatory plaque-reduction neutralization tests. Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruses, which can be a cumbersome task. The advent of multiplexing represents an opportunity to streamline these types of assays; however, because serologic cross-reactivity of the arboviral antigens often confounds results, it is of interest to employ data analysis methods that address this issue. Here, we constructed 13-virus multiplexed IgM and IgG MIAs that included internal and external controls, based upon the Luminex platform. Results from samples tested using these methods were analyzed using 8 different statistical schemes to identify the best way to classify the data. Geographic batteries were also devised to serve as a more practical diagnostic format, and further samples were tested using the abbreviated multiplexes. Comparative error rates for the classification schemes identified a specific boosting method based on logistic regression “Logitboost” as the classification method of choice. When the data from all samples tested were combined into one set, error rates from the multiplex IgM and IgG MIAs were <5% for all geographic batteries. This work represents both the most comprehensive, validated multiplexing method for arboviruses to date, and also the most systematic attempt to determine the most useful classification method for use with these types of serologic tests. PMID:24086608

Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

2018-05-01

Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Subthreshold muscle twitches dissociate oscillatory neural signatures of conflicts from errors.

PubMed

Cohen, Michael X; van Gaal, Simon

2014-02-01

We investigated the neural systems underlying conflict detection and error monitoring during rapid online error correction/monitoring mechanisms. We combined data from four separate cognitive tasks and 64 subjects in which EEG and EMG (muscle activity from the thumb used to respond) were recorded. In typical neuroscience experiments, behavioral responses are classified as "error" or "correct"; however, closer inspection of our data revealed that correct responses were often accompanied by "partial errors" - a muscle twitch of the incorrect hand ("mixed correct trials," ~13% of the trials). We found that these muscle twitches dissociated conflicts from errors in time-frequency domain analyses of EEG data. In particular, both mixed-correct trials and full error trials were associated with enhanced theta-band power (4-9Hz) compared to correct trials. However, full errors were additionally associated with power and frontal-parietal synchrony in the delta band. Single-trial robust multiple regression analyses revealed a significant modulation of theta power as a function of partial error correction time, thus linking trial-to-trial fluctuations in power to conflict. Furthermore, single-trial correlation analyses revealed a qualitative dissociation between conflict and error processing, such that mixed correct trials were associated with positive theta-RT correlations whereas full error trials were associated with negative delta-RT correlations. These findings shed new light on the local and global network mechanisms of conflict monitoring and error detection, and their relationship to online action adjustment. © 2013.

Article Errors in the English Writing of Saudi EFL Preparatory Year Students

ERIC Educational Resources Information Center

Alhaisoni, Eid; Gaudel, Daya Ram; Al-Zuoud, Khalid M.

2017-01-01

This study aims at providing a comprehensive account of the types of errors produced by Saudi EFL students enrolled in the preparatory year programe in their use of articles, based on the Surface Structure Taxonomies (SST) of errors. The study describes the types, frequency and sources of the definite and indefinite article errors in writing…

Identifying Novice Student Programming Misconceptions and Errors from Summative Assessments

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Laakso, Mikko-Jussi

2016-01-01

This article presents a study aimed at examining the novice student answers in an introductory programming final e-exam to identify misconceptions and types of errors. Our study used the Delphi concept inventory to identify student misconceptions and skill, rule, and knowledge-based errors approach to identify the types of errors made by novices…

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Understanding Problem-Solving Errors by Students with Learning Disabilities in Standards-Based and Traditional Curricula

ERIC Educational Resources Information Center

Bouck, Emily C.; Bouck, Mary K.; Joshi, Gauri S.; Johnson, Linley

2016-01-01

Students with learning disabilities struggle with word problems in mathematics classes. Understanding the type of errors students make when working through such mathematical problems can further describe student performance and highlight student difficulties. Through the use of error codes, researchers analyzed the type of errors made by 14 sixth…

Type I error probabilities based on design-stage strategies with applications to noninferiority trials.

PubMed

Rothmann, Mark

2005-01-01

When testing the equality of means from two different populations, a t-test or large sample normal test tend to be performed. For these tests, when the sample size or design for the second sample is dependent on the results of the first sample, the type I error probability is altered for each specific possibility in the null hypothesis. We will examine the impact on the type I error probabilities for two confidence interval procedures and procedures using test statistics when the design for the second sample or experiment is dependent on the results from the first sample or experiment (or series of experiments). Ways for controlling a desired maximum type I error probability or a desired type I error rate will be discussed. Results are applied to the setting of noninferiority comparisons in active controlled trials where the use of a placebo is unethical.

Error analysis and correction of lever-type stylus profilometer based on Nelder-Mead Simplex method

NASA Astrophysics Data System (ADS)

Hu, Chunbing; Chang, Suping; Li, Bo; Wang, Junwei; Zhang, Zhongyu

2017-10-01

Due to the high measurement accuracy and wide range of applications, lever-type stylus profilometry is commonly used in industrial research areas. However, the error caused by the lever structure has a great influence on the profile measurement, thus this paper analyzes the error of high-precision large-range lever-type stylus profilometry. The errors are corrected by the Nelder-Mead Simplex method, and the results are verified by the spherical surface calibration. It can be seen that this method can effectively reduce the measurement error and improve the accuracy of the stylus profilometry in large-scale measurement.

Evaluation of normalization methods for cDNA microarray data by k-NN classification

PubMed Central

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-01-01

Background Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Results Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Conclusion Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics. PMID:16045803

Evaluation of normalization methods for cDNA microarray data by k-NN classification.

PubMed

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, I Saira; Bissell, Mina J

2005-07-26

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double-bias-removal normalization strategies, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, outperform other strategies for removing spatial effect, intensity effect and scale differences from cDNA microarray data. The apparent sensitivity of k-NN LOOCV classification error to dye biases suggests that this criterion provides an informative measure for evaluating normalization methods. All the computational tools used in this study were implemented using the R language for statistical computing and graphics.

Addressing Common Student Technical Errors in Field Data Collection: An Analysis of a Citizen-Science Monitoring Project.

PubMed

Philippoff, Joanna; Baumgartner, Erin

2016-03-01

The scientific value of citizen-science programs is limited when the data gathered are inconsistent, erroneous, or otherwise unusable. Long-term monitoring studies, such as Our Project In Hawai'i's Intertidal (OPIHI), have clear and consistent procedures and are thus a good model for evaluating the quality of participant data. The purpose of this study was to examine the kinds of errors made by student researchers during OPIHI data collection and factors that increase or decrease the likelihood of these errors. Twenty-four different types of errors were grouped into four broad error categories: missing data, sloppiness, methodological errors, and misidentification errors. "Sloppiness" was the most prevalent error type. Error rates decreased with field trip experience and student age. We suggest strategies to reduce data collection errors applicable to many types of citizen-science projects including emphasizing neat data collection, explicitly addressing and discussing the problems of falsifying data, emphasizing the importance of using standard scientific vocabulary, and giving participants multiple opportunities to practice to build their data collection techniques and skills.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Refractive errors in children and adolescents in Bucaramanga (Colombia).

PubMed

Galvis, Virgilio; Tello, Alejandro; Otero, Johanna; Serrano, Andrés A; Gómez, Luz María; Castellanos, Yuly

2017-01-01

The aim of this study was to establish the frequency of refractive errors in children and adolescents aged between 8 and 17 years old, living in the metropolitan area of Bucaramanga (Colombia). This study was a secondary analysis of two descriptive cross-sectional studies that applied sociodemographic surveys and assessed visual acuity and refraction. Ametropias were classified as myopic errors, hyperopic errors, and mixed astigmatism. Eyes were considered emmetropic if none of these classifications were made. The data were collated using free software and analyzed with STATA/IC 11.2. One thousand two hundred twenty-eight individuals were included in this study. Girls showed a higher rate of ametropia than boys. Hyperopic refractive errors were present in 23.1% of the subjects, and myopic errors in 11.2%. Only 0.2% of the eyes had high myopia (≤-6.00 D). Mixed astigmatism and anisometropia were uncommon, and myopia frequency increased with age. There were statistically significant steeper keratometric readings in myopic compared to hyperopic eyes. The frequency of refractive errors that we found of 36.7% is moderate compared to the global data. The rates and parameters statistically differed by sex and age groups. Our findings are useful for establishing refractive error rate benchmarks in low-middle-income countries and as a baseline for following their variation by sociodemographic factors.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.

PubMed

Donoho, David; Jin, Jiashun

2008-09-30

In important application fields today-genomics and proteomics are examples-selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, ..., p, let pi(i) denote the two-sided P-value associated with the ith feature Z-score and pi((i)) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p - pi((i)))/sqrt{i/p(1-i/p)}. We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT.

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak

PubMed Central

Donoho, David; Jin, Jiashun

2008-01-01

In important application fields today—genomics and proteomics are examples—selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, …, p, let πi denote the two-sided P-value associated with the ith feature Z-score and π(i) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p − π(i))/i/p(1−i/p). We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT. PMID:18815365

The Effects of Non-Normality on Type III Error for Comparing Independent Means

ERIC Educational Resources Information Center

Mendes, Mehmet

2007-01-01

The major objective of this study was to investigate the effects of non-normality on Type III error rates for ANOVA F its three commonly recommended parametric counterparts namely Welch, Brown-Forsythe, and Alexander-Govern test. Therefore these tests were compared in terms of Type III error rates across the variety of population distributions,…

Item Discrimination and Type I Error in the Detection of Differential Item Functioning

ERIC Educational Resources Information Center

Li, Yanju; Brooks, Gordon P.; Johanson, George A.

2012-01-01

In 2009, DeMars stated that when impact exists there will be Type I error inflation, especially with larger sample sizes and larger discrimination parameters for items. One purpose of this study is to present the patterns of Type I error rates using Mantel-Haenszel (MH) and logistic regression (LR) procedures when the mean ability between the…

Improving the prediction of going concern of Taiwanese listed companies using a hybrid of LASSO with data mining techniques.

PubMed

Goo, Yeung-Ja James; Chi, Der-Jang; Shen, Zong-De

2016-01-01

The purpose of this study is to establish rigorous and reliable going concern doubt (GCD) prediction models. This study first uses the least absolute shrinkage and selection operator (LASSO) to select variables and then applies data mining techniques to establish prediction models, such as neural network (NN), classification and regression tree (CART), and support vector machine (SVM). The samples of this study include 48 GCD listed companies and 124 NGCD (non-GCD) listed companies from 2002 to 2013 in the TEJ database. We conduct fivefold cross validation in order to identify the prediction accuracy. According to the empirical results, the prediction accuracy of the LASSO-NN model is 88.96 % (Type I error rate is 12.22 %; Type II error rate is 7.50 %), the prediction accuracy of the LASSO-CART model is 88.75 % (Type I error rate is 13.61 %; Type II error rate is 14.17 %), and the prediction accuracy of the LASSO-SVM model is 89.79 % (Type I error rate is 10.00 %; Type II error rate is 15.83 %).

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

How to select combination operators for fuzzy expert systems using CRI

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Tian, Y.

1992-01-01

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

Refractive errors in Aminu Kano Teaching Hospital, Kano Nigeria.

PubMed

Lawan, Abdu; Eme, Okpo

2011-12-01

The aim of the study is to retrospectively determine the pattern of refractive errors seen in the eye clinic of Aminu Kano Teaching Hospital, Kano-Nigeria from January to December, 2008. The clinic refraction register was used to retrieve the case folders of all patients refracted during the review period. Information extracted includes patient's age, sex, and types of refractive error. All patients had basic eye examination (to rule out other causes of subnormal vision) including intra ocular pressure measurement and streak retinoscopy at two third meter working distance. The final subjective refraction correction given to the patients was used to categorise the type of refractive error. Refractive errors was observed in 1584 patients and accounted for 26.9% of clinic attendance. There were more females than males (M: F=1.0: 1.2). The common types of refractive errors are presbyopia in 644 patients (40%), various types of astigmatism in 527 patients (33%), myopia in 216 patients (14%), hypermetropia in 171 patients (11%) and aphakia in 26 patients (2%). Refractive errors are common causes of presentation in the eye clinic. Identification and correction of refractive errors should be an integral part of eye care delivery.

Algorithm design for automated transportation photo enforcement camera image and video quality diagnostic check modules

NASA Astrophysics Data System (ADS)

Raghavan, Ajay; Saha, Bhaskar

2013-03-01

Photo enforcement devices for traffic rules such as red lights, toll, stops, and speed limits are increasingly being deployed in cities and counties around the world to ensure smooth traffic flow and public safety. These are typically unattended fielded systems, and so it is important to periodically check them for potential image/video quality problems that might interfere with their intended functionality. There is interest in automating such checks to reduce the operational overhead and human error involved in manually checking large camera device fleets. Examples of problems affecting such camera devices include exposure issues, focus drifts, obstructions, misalignment, download errors, and motion blur. Furthermore, in some cases, in addition to the sub-algorithms for individual problems, one also has to carefully design the overall algorithm and logic to check for and accurately classifying these individual problems. Some of these issues can occur in tandem or have the potential to be confused for each other by automated algorithms. Examples include camera misalignment that can cause some scene elements to go out of focus for wide-area scenes or download errors that can be misinterpreted as an obstruction. Therefore, the sequence in which the sub-algorithms are utilized is also important. This paper presents an overview of these problems along with no-reference and reduced reference image and video quality solutions to detect and classify such faults.

An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images.

PubMed

Farhan, Saima; Fahiem, Muhammad Abuzar; Tauseef, Huma

2014-01-01

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Error identification, disclosure, and reporting: practice patterns of three emergency medicine provider types.

PubMed

Hobgood, Cherri; Xie, Jipan; Weiner, Bryan; Hooker, James

2004-02-01

To gather preliminary data on how the three major types of emergency medicine (EM) providers, physicians, nurses (RNs), and out-of-hospital personnel (EMTs), differ in error identification, disclosure, and reporting. A convenience sample of emergency department (ED) providers completed a brief survey designed to evaluate error frequency, disclosure, and reporting practices as well as error-based discussion and educational activities. One hundred sixteen subjects participated: 41 EMTs (35%), 33 RNs (28%), and 42 physicians (36%). Forty-five percent of EMTs, 56% of RNs, and 21% of physicians identified no clinical errors during the preceding year. When errors were identified, physicians learned of them via dialogue with RNs (58%), patients (13%), pharmacy (35%), and attending physicians (35%). For known errors, all providers were equally unlikely to inform the team caring for the patient. Disclosure to patients was limited and varied by provider type (19% EMTs, 23% RNs, and 74% physicians). Disclosure education was rare, with

Image processing and analysis using neural networks for optometry area

NASA Astrophysics Data System (ADS)

Netto, Antonio V.; Ferreira de Oliveira, Maria C.

2002-11-01

In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.

Hospital medication errors in a pharmacovigilance system in Colombia.

PubMed

Machado Alba, Jorge Enrique; Moreno Gutiérrez, Paula Andrea; Moncada Escobar, Juan Carlos

2015-11-01

this study analyzes the medication errors reported to a pharmacovigilance system by 26 hospitals for patients in the healthcare system of Colombia. this retrospective study analyzed the medication errors reported to a systematized database between 1 January 2008 and 12 September 2013. The medication is dispensed by the company Audifarma S.A. to hospitals and clinics around Colombia. Data were classified according to the taxonomy of the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP). The data analysis was performed using SPSS 22.0 for Windows, considering p-values < 0.05 significant. there were 9 062 medication errors in 45 hospital pharmacies. Real errors accounted for 51.9% (n = 4 707), of which 12.0% (n = 567) reached the patient (Categories C to I) and caused harm (Categories E to I) to 17 subjects (0.36%). The main process involved in errors that occurred (categories B to I) was prescription (n = 1 758, 37.3%), followed by dispensation (n = 1 737, 36.9%), transcription (n = 970, 20.6%) and administration (n = 242, 5.1%). The errors in the administration process were 45.2 times more likely to reach the patient (CI 95%: 20.2-100.9). medication error reporting systems and prevention strategies should be widespread in hospital settings, prioritizing efforts to address the administration process. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Classification of sea ice types with single-band (33.6 GHz) airborne passive microwave imagery

NASA Astrophysics Data System (ADS)

Eppler, Duane T.; Farmer, L. Dennis; Lohanick, Alan W.; Hoover, Mervyn

1986-09-01

During March 1983 extensive high-quality airborne passive Ka band (33.6 GHz) microwave imagery and coincident high-resolution aerial photography were obtained of ice along a 378-km flight line in the Beaufort Sea. Analysis of these data suggests that four classes of winter surfaces can be distinguished solely on the basis of 33.6-GHz brightness temperature: open water, frazil, old ice, and young/first-year ice. New ice (excluding frazil) and nilas display brightness temperatures that overlap the range of temperatures characteristic of old ice and, to a lesser extent, young/first-year ice. Scenes in which a new ice or nilas are present in appreciable amounts are subject to substantial errors in classification if static measures of Ka band radiometric brightness temperature alone are considered. Textural characteristics of nilas and new ice, however, differ significantly from textural features characteristic of other ice types and probably can be used with brightness temperature data to classify ice type in high-resolution single-band microwave images. In any case, open water is radiometrically the coldest surface observed in any scene. Lack of overlap between brightness temperatures characteristic of other surfaces indicates that estimates of the areal extent of open water based on only 33.6-GHz brightness temperatures are accurate.

Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments

PubMed Central

Kroll, Alexandra; Haramagatti, Chandrashekara R.; Lipinski, Hans-Gerd; Wiemann, Martin

2017-01-01

Darkfield and confocal laser scanning microscopy both allow for a simultaneous observation of live cells and single nanoparticles. Accordingly, a characterization of nanoparticle uptake and intracellular mobility appears possible within living cells. Single particle tracking allows to measure the size of a diffusing particle close to a cell. However, within the more complex system of a cell’s cytoplasm normal, confined or anomalous diffusion together with directed motion may occur. In this work we present a method to automatically classify and segment single trajectories into their respective motion types. Single trajectories were found to contain more than one motion type. We have trained a random forest with 9 different features. The average error over all motion types for synthetic trajectories was 7.2%. The software was successfully applied to trajectories of positive controls for normal- and constrained diffusion. Trajectories captured by nanoparticle tracking analysis served as positive control for normal diffusion. Nanoparticles inserted into a diblock copolymer membrane was used to generate constrained diffusion. Finally we segmented trajectories of diffusing (nano-)particles in V79 cells captured with both darkfield- and confocal laser scanning microscopy. The software called “TraJClassifier” is freely available as ImageJ/Fiji plugin via https://git.io/v6uz2. PMID:28107406

Werbung im Englischunterricht: Das Beispiel Einhorn - Onehorn - Unicorn (Advertising Material in English Teaching: The Example "Einhorn-Onehorn-Unicorn")

ERIC Educational Resources Information Center

Ruettgens, Hannelore

1976-01-01

Presents an advertisement from "Der Spiegel," composed in English that is saturated with Germanisms. Teaching procedures based on this are suggested: finding and classifying errors, composing alternative versions, translating into German, retranslating into English. Suggestions are given for further work based on the students' own…

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers

PubMed Central

2014-01-01

Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients. PMID:24903422

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

PubMed

Huang, Huifang; Liu, Jie; Zhu, Qiang; Wang, Ruiping; Hu, Guangshu

2014-06-05

Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.

Math Error Types and Correlates in Adolescents with and without Attention Deficit Hyperactivity Disorder

PubMed Central

Capodieci, Agnese; Martinussen, Rhonda

2017-01-01

Objective: The aim of this study was to examine the types of errors made by youth with and without a parent-reported diagnosis of attention deficit and hyperactivity disorder (ADHD) on a math fluency task and investigate the association between error types and youths’ performance on measures of processing speed and working memory. Method: Participants included 30 adolescents with ADHD and 39 typically developing peers between 14 and 17 years old matched in age and IQ. All youth completed standardized measures of math calculation and fluency as well as two tests of working memory and processing speed. Math fluency error patterns were examined. Results: Adolescents with ADHD showed less proficient math fluency despite having similar math calculation scores as their peers. Group differences were also observed in error types with youth with ADHD making more switch errors than their peers. Conclusion: This research has important clinical applications for the assessment and intervention on math ability in students with ADHD. PMID:29075227

Math Error Types and Correlates in Adolescents with and without Attention Deficit Hyperactivity Disorder.

PubMed

Capodieci, Agnese; Martinussen, Rhonda

2017-01-01

Objective: The aim of this study was to examine the types of errors made by youth with and without a parent-reported diagnosis of attention deficit and hyperactivity disorder (ADHD) on a math fluency task and investigate the association between error types and youths' performance on measures of processing speed and working memory. Method: Participants included 30 adolescents with ADHD and 39 typically developing peers between 14 and 17 years old matched in age and IQ. All youth completed standardized measures of math calculation and fluency as well as two tests of working memory and processing speed. Math fluency error patterns were examined. Results: Adolescents with ADHD showed less proficient math fluency despite having similar math calculation scores as their peers. Group differences were also observed in error types with youth with ADHD making more switch errors than their peers. Conclusion: This research has important clinical applications for the assessment and intervention on math ability in students with ADHD.

Exploring the feasibility of smart phone microphone for measurement of acoustic voice parameters and voice pathology screening.

PubMed

Uloza, Virgilijus; Padervinskis, Evaldas; Vegiene, Aurelija; Pribuisiene, Ruta; Saferis, Viktoras; Vaiciukynas, Evaldas; Gelzinis, Adas; Verikas, Antanas

2015-11-01

The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73-1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7% and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5%. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60% and RFC revealed the EER of 7.9%, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.

A molecular topology approach to predicting pesticide pollution of groundwater

USGS Publications Warehouse

Worrall , Fred

2001-01-01

Various models have proposed methods for the discrimination of polluting and nonpolluting compounds on the basis of simple parameters, typically adsorption and degradation constants. However, such attempts are prone to site variability and measurement error to the extent that compounds cannot be reliably classified nor the chemistry of pollution extrapolated from them. Using observations of pesticide occurrence in U.S. groundwater it is possible to show that polluting from nonpolluting compounds can be distinguished purely on the basis of molecular topology. Topological parameters can be derived without measurement error or site-specific variability. A logistic regression model has been developed which explains 97% of the variation in the data, with 86% of the variation being explained by the rule that a compound will be found in groundwater if 6 < 0.55. Where 6χp is the sixth-order molecular path connectivity. One group of compounds cannot be classified by this rule and prediction requires reference to higher order connectivity parameters. The use of molecular approaches for understanding pollution at the molecular level and their application to agrochemical development and risk assessment is discussed.

Improvement of the Mair scoring system using structural equations modeling for classifying the diagnostic adequacy of cytology material from thyroid lesions.

PubMed

Kulkarni, H R; Kamal, M M; Arjune, D G

1999-12-01

The scoring system developed by Mair et al. (Acta Cytol 1989;33:809-813) is frequently used to grade the quality of cytology smears. Using a one-factor analytic structural equations model, we demonstrate that the errors in measurement of the parameters used in the Mair scoring system are highly and significantly correlated. We recommend the use of either a multiplicative scoring system, using linear scores, or an additive scoring system, using exponential scores, to correct for the correlated errors. We suggest that the 0, 1, and 2 points used in the Mair scoring system be replaced by 1, 2, and 4, respectively. Using data on fine-needle biopsies of 200 thyroid lesions by both fine-needle aspiration (FNA) and fine-needle capillary sampling (FNC), we demonstrate that our modification of the Mair scoring system is more sensitive and more consistent with the structural equations model. Therefore, we recommend that the modified Mair scoring system be used for classifying the diagnostic adequacy of cytology smears. Diagn. Cytopathol. 1999;21:387-393. Copyright 1999 Wiley-Liss, Inc.

Technology-related medication errors in a tertiary hospital: a 5-year analysis of reported medication incidents.

PubMed

Samaranayake, N R; Cheung, S T D; Chui, W C M; Cheung, B M Y

2012-12-01

Healthcare technology is meant to reduce medication errors. The objective of this study was to assess unintended errors related to technologies in the medication use process. Medication incidents reported from 2006 to 2010 in a main tertiary care hospital were analysed by a pharmacist and technology-related errors were identified. Technology-related errors were further classified as socio-technical errors and device errors. This analysis was conducted using data from medication incident reports which may represent only a small proportion of medication errors that actually takes place in a hospital. Hence, interpretation of results must be tentative. 1538 medication incidents were reported. 17.1% of all incidents were technology-related, of which only 1.9% were device errors, whereas most were socio-technical errors (98.1%). Of these, 61.2% were linked to computerised prescription order entry, 23.2% to bar-coded patient identification labels, 7.2% to infusion pumps, 6.8% to computer-aided dispensing label generation and 1.5% to other technologies. The immediate causes for technology-related errors included, poor interface between user and computer (68.1%), improper procedures or rule violations (22.1%), poor interface between user and infusion pump (4.9%), technical defects (1.9%) and others (3.0%). In 11.4% of the technology-related incidents, the error was detected after the drug had been administered. A considerable proportion of all incidents were technology-related. Most errors were due to socio-technical issues. Unintended and unanticipated errors may happen when using technologies. Therefore, when using technologies, system improvement, awareness, training and monitoring are needed to minimise medication errors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

2016-03-01

Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

Impact of study design on development and evaluation of an activity-type classifier.

PubMed

van Hees, Vincent T; Golubic, Rajna; Ekelund, Ulf; Brage, Søren

2013-04-01

Methods to classify activity types are often evaluated with an experimental protocol involving prescribed physical activities under confined (laboratory) conditions, which may not reflect real-life conditions. The present study aims to evaluate how study design may impact on classifier performance in real life. Twenty-eight healthy participants (21-53 yr) were asked to wear nine triaxial accelerometers while performing 58 activity types selected to simulate activities in real life. For each sensor location, logistic classifiers were trained in subsets of up to 8 activities to distinguish between walking and nonwalking activities and were then evaluated in all 58 activities. Different weighting factors were used to convert the resulting confusion matrices into an estimation of the confusion matrix as would apply in the real-life setting by creating four different real-life scenarios, as well as one traditional laboratory scenario. The sensitivity of a classifier estimated with a traditional laboratory protocol is within the range of estimates derived from real-life scenarios for any body location. The specificity, however, was systematically overestimated by the traditional laboratory scenario. Walking time was systematically overestimated, except for lower back sensor data (range: 7-757%). In conclusion, classifier performance under confined conditions may not accurately reflect classifier performance in real life. Future studies that aim to evaluate activity classification methods are warranted to pay special attention to the representativeness of experimental conditions for real-life conditions.

A machine learned classifier for RR Lyrae in the VVV survey

NASA Astrophysics Data System (ADS)

Elorrieta, Felipe; Eyheramendy, Susana; Jordán, Andrés; Dékány, István; Catelan, Márcio; Angeloni, Rodolfo; Alonso-García, Javier; Contreras-Ramos, Rodrigo; Gran, Felipe; Hajdu, Gergely; Espinoza, Néstor; Saito, Roberto K.; Minniti, Dante

2016-11-01

Variable stars of RR Lyrae type are a prime tool with which to obtain distances to old stellar populations in the Milky Way. One of the main aims of the Vista Variables in the Via Lactea (VVV) near-infrared survey is to use them to map the structure of the Galactic Bulge. Owing to the large number of expected sources, this requires an automated mechanism for selecting RR Lyrae, and particularly those of the more easily recognized type ab (I.e., fundamental-mode pulsators), from the 106-107 variables expected in the VVV survey area. In this work we describe a supervised machine-learned classifier constructed for assigning a score to a Ks-band VVV light curve that indicates its likelihood of being ab-type RR Lyrae. We describe the key steps in the construction of the classifier, which were the choice of features, training set, selection of aperture, and family of classifiers. We find that the AdaBoost family of classifiers give consistently the best performance for our problem, and obtain a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VVV light-curve sets. This performance is estimated using cross-validation and through the comparison to two independent datasets that were classified by human experts.

Composition, production rate and characterization of Greek dental solid waste.

PubMed

Mandalidis, Alexandros; Topalidis, Antonios; Voudrias, Evangelos A; Iosifidis, Nikolaos

2018-05-01

The overall objective of this work is to determine the composition, characterization and production rate of Greek dental solid waste (DSW). This information is important to design and cost management systems for DSW, for safety and health considerations and for assessing environmental impact. A total of 141 kg of DSW produced by a total of 2542 patients in 20 dental practices from Xanthi, Greece was collected, manually separated and weighed over a period of four working weeks. The waste was separated in 19 sub fractions, which were classified in 2 major categories, according to Greek regulations: Domestic-type waste comprising 8% and hazardous waste comprising 92% by weight of total DSW. The latter was further classified in infectious waste, toxic waste and mixed type waste (infectious and toxic together), accounting for 88.5%, 3.5% and 0.03% of total DSW by weight, respectively. The overall unit production rates (mean ± standard error of the mean) were 381 ± 15 g/practice/d and 53.3 ± 1.4 g/patient/d for total DSW, 337 ± 14 g/practice/d and 46.6 ± 1.2 g/patient/d for total infectious DSW, 13.4 ± 0.7 g/practice/d and 2.1 ± 0.1 g/patient/d for total toxic DSW and 30.4 ± 2.5 g/practice/d and 4.6 ± 0.4 g/patient/d for domestic-type waste. Daily DSW production was correlated with daily number of patients and regression correlations were produced. DSW was subject to laboratory characterization in terms of bulk density, calorific value, moisture, ash and volatile solids content. Measured calorific values were compared to predictions from empirical models. Copyright © 2018 Elsevier Ltd. All rights reserved.

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

PubMed Central

Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

2016-01-01

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

PubMed

Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

2017-06-12

Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced "stratified" datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused with medium risk incidents (SAC3). Binary classifier ensembles appear to be a feasible method for identifying incidents by type and severity level. Automated identification should enable safety problems to be detected and addressed in a more timely manner. Multi-label classifiers may be necessary for reports that relate to more than one incident type.

Rank score and permutation testing alternatives for regression quantile estimates

USGS Publications Warehouse

Cade, B.S.; Richards, J.D.; Mielke, P.W.

2006-01-01

Performance of quantile rank score tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1) were evaluated by simulation for models with p = 2 and 6 predictors, moderate collinearity among predictors, homogeneous and hetero-geneous errors, small to moderate samples (n = 20–300), and central to upper quantiles (0.50–0.99). Test statistics evaluated were the conventional quantile rank score T statistic distributed as χ2 random variable with q degrees of freedom (where q parameters are constrained by H 0:) and an F statistic with its sampling distribution approximated by permutation. The permutation F-test maintained better Type I errors than the T-test for homogeneous error models with smaller n and more extreme quantiles τ. An F distributional approximation of the F statistic provided some improvements in Type I errors over the T-test for models with > 2 parameters, smaller n, and more extreme quantiles but not as much improvement as the permutation approximation. Both rank score tests required weighting to maintain correct Type I errors when heterogeneity under the alternative model increased to 5 standard deviations across the domain of X. A double permutation procedure was developed to provide valid Type I errors for the permutation F-test when null models were forced through the origin. Power was similar for conditions where both T- and F-tests maintained correct Type I errors but the F-test provided some power at smaller n and extreme quantiles when the T-test had no power because of excessively conservative Type I errors. When the double permutation scheme was required for the permutation F-test to maintain valid Type I errors, power was less than for the T-test with decreasing sample size and increasing quantiles. Confidence intervals on parameters and tolerance intervals for future predictions were constructed based on test inversion for an example application relating trout densities to stream channel width:depth.

Ensemble codes involving hippocampal neurons are at risk during delayed performance tests.

PubMed

Hampson, R E; Deadwyler, S A

1996-11-26

Multielectrode recording techniques were used to record ensemble activity from 10 to 16 simultaneously active CA1 and CA3 neurons in the rat hippocampus during performance of a spatial delayed-nonmatch-to-sample task. Extracted sources of variance were used to assess the nature of two different types of errors that accounted for 30% of total trials. The two types of errors included ensemble "miscodes" of sample phase information and errors associated with delay-dependent corruption or disappearance of sample information at the time of the nonmatch response. Statistical assessment of trial sequences and associated "strength" of hippocampal ensemble codes revealed that miscoded error trials always followed delay-dependent error trials in which encoding was "weak," indicating that the two types of errors were "linked." It was determined that the occurrence of weakly encoded, delay-dependent error trials initiated an ensemble encoding "strategy" that increased the chances of being correct on the next trial and avoided the occurrence of further delay-dependent errors. Unexpectedly, the strategy involved "strongly" encoding response position information from the prior (delay-dependent) error trial and carrying it forward to the sample phase of the next trial. This produced a miscode type error on trials in which the "carried over" information obliterated encoding of the sample phase response on the next trial. Application of this strategy, irrespective of outcome, was sufficient to reorient the animal to the proper between trial sequence of response contingencies (nonmatch-to-sample) and boost performance to 73% correct on subsequent trials. The capacity for ensemble analyses of strength of information encoding combined with statistical assessment of trial sequences therefore provided unique insight into the "dynamic" nature of the role hippocampus plays in delay type memory tasks.

A cognitive taxonomy of medical errors.

PubMed

Zhang, Jiajie; Patel, Vimla L; Johnson, Todd R; Shortliffe, Edward H

2004-06-01

Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology. Use cognitive theories of human error and human action to develop the theoretical foundations of the taxonomy, develop the structure of the taxonomy, populate the taxonomy with examples of medical error cases, identify cognitive mechanisms for each category of medical error under the taxonomy, and apply the taxonomy to practical problems. Four criteria were used to evaluate the cognitive taxonomy. The taxonomy should be able (1) to categorize major types of errors at the individual level along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to describe how and explain why a specific error occurs, and (4) to generate intervention strategies for each type of error. The proposed cognitive taxonomy largely satisfies the four criteria at a theoretical and conceptual level. Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.

Errors Analysis of Students in Mathematics Department to Learn Plane Geometry

NASA Astrophysics Data System (ADS)

Mirna, M.

2018-04-01

This article describes the results of qualitative descriptive research that reveal the locations, types and causes of student error in answering the problem of plane geometry at the problem-solving level. Answers from 59 students on three test items informed that students showed errors ranging from understanding the concepts and principles of geometry itself to the error in applying it to problem solving. Their type of error consists of concept errors, principle errors and operational errors. The results of reflection with four subjects reveal the causes of the error are: 1) student learning motivation is very low, 2) in high school learning experience, geometry has been seen as unimportant, 3) the students' experience using their reasoning in solving the problem is very less, and 4) students' reasoning ability is still very low.

Acoustic evidence for phonologically mismatched speech errors.

PubMed

Gormley, Andrea

2015-04-01

Speech errors are generally said to accommodate to their new phonological context. This accommodation has been validated by several transcription studies. The transcription methodology is not the best choice for detecting errors at this level, however, as this type of error can be difficult to perceive. This paper presents an acoustic analysis of speech errors that uncovers non-accommodated or mismatch errors. A mismatch error is a sub-phonemic error that results in an incorrect surface phonology. This type of error could arise during the processing of phonological rules or they could be made at the motor level of implementation. The results of this work have important implications for both experimental and theoretical research. For experimentalists, it validates the tools used for error induction and the acoustic determination of errors free of the perceptual bias. For theorists, this methodology can be used to test the nature of the processes proposed in language production.

Statistical approaches to account for false-positive errors in environmental DNA samples.

PubMed

Lahoz-Monfort, José J; Guillera-Arroita, Gurutzeta; Tingley, Reid

2016-05-01

Environmental DNA (eDNA) sampling is prone to both false-positive and false-negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false-positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false-positive rates. We advocate alternative approaches to account for false-positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false-positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false-negative and false-positive errors, the methods presented here should be more routinely adopted in eDNA studies. © 2015 John Wiley & Sons Ltd.

Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study.

PubMed

Durand, Casey P

2013-01-01

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model. A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations. In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified. Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.

Determination of Type I Error Rates and Power of Answer Copying Indices under Various Conditions

ERIC Educational Resources Information Center

Yormaz, Seha; Sünbül, Önder

2017-01-01

This study aims to determine the Type I error rates and power of S[subscript 1] , S[subscript 2] indices and kappa statistic at detecting copying on multiple-choice tests under various conditions. It also aims to determine how copying groups are created in order to calculate how kappa statistics affect Type I error rates and power. In this study,…

Towards More Nuanced Classification of NGOs and Their Services to Improve Integrated Planning across Disaster Phases

PubMed Central

Towe, Vivian L.; Acosta, Joie D.; Chandra, Anita

2017-01-01

Nongovernmental organizations (NGOs) are being integrated into U.S. strategies to expand the services that are available during health security threats like disasters. Identifying better ways to classify NGOs and their services could optimize disaster planning. We surveyed NGOs about the types of services they provided during different disaster phases. Survey responses were used to categorize NGO services as core—critical to fulfilling their organizational mission—or adaptive—services implemented during a disaster based on community need. We also classified NGOs as being core or adaptive types of organizations by calculating the percentage of each NGO’s services classified as core. Service types classified as core were mainly social services, while adaptive service types were those typically relied upon during disasters (e.g., warehousing, food services, etc.). In total, 120 NGOs were classified as core organizations, meaning they mainly provided the same services across disaster phases, while 100 NGOs were adaptive organizations, meaning their services changed. Adaptive NGOs were eight times more likely to report routinely participating in disaster planning as compared to core NGOs. One reason for this association may be that adaptive NGOs are more aware of the changing needs in their communities across disaster phases because of their involvement in disaster planning. PMID:29160810

Modic changes in lumbar spine: prevalence and distribution patterns of end plate oedema and end plate sclerosis.

PubMed

Xu, Lei; Chu, Bin; Feng, Yang; Xu, Feng; Zou, Yue-Fen

2016-01-01

The purpose of this study is to evaluate the distribution of end plate oedema in different types of Modic change especially in mixed type and to analyze the presence of end plate sclerosis in various types of Modic change. 276 patients with low back pain were scanned with 1.5-T MRI. Three radiologists assessed the MR images by T1 weighted, T2 weighted and fat-saturation T2 weighted sequences and classified them according to the Modic changes. Pure oedematous end plate signal changes were classified as Modic Type I; pure fatty end plate changes were classified as Modic Type II; and pure sclerotic end plate changes as Modic Type III. A mixed feature of both Types I and II with predominant oedematous signal change is classified as Modic I-II, and a mixture of Types I and II with predominant fatty change is classified as Modic II-I. Thus, the mixed types can further be subdivided into seven subtypes: Types I-II, Types II-I, Types I-III, Types III-I, Types II-III, Types III-II and Types I-III. During the same period, 52 of 276 patients who underwent CT and MRI were retrospectively reviewed to determine end plate sclerosis. (1) End plate oedema: of the 2760 end plates (276 patients) examined, 302 end plates showed Modic changes, of which 82 end plates showed mixed Modic changes. The mixed Modic changes contain 92.7% of oedematous changes. The mixed types especially Types I-II and Types II-I made up the majority of end plate oedematous changes. (2) End plate sclerosis: 52 of 276 patients were examined by both MRI and CT. Of the 520 end plates, 93 end plates showed Modic changes, of which 34 end plates have shown sclerotic changes in CT images. 11.8% of 34 end plates have shown Modic Type I, 20.6% of 34 end plates have shown Modic Type II, 2.9% of 34 end plates have shown Modic Type III and 64.7% of 34 end plates have shown mixed Modic type. End plate oedema makes up the majority of mixed types especially Types I-II and Types II-I. The end plate sclerosis on CT images may not just mean Modic Type III but does exist in all types of Modic changes, especially in mixed Modic types, and may reflect vertebral body mineralization rather than change in the bone marrow. End plate oedema and end plate sclerosis are present in a large proportion of mixed types.

Equating an expert system to a classifier in order to evaluate the expert system

NASA Technical Reports Server (NTRS)

Odell, Patrick L.

1989-01-01

A strategy to evaluate an expert system is formulated. The strategy proposed is based on finding an equivalent classifier to an expert system and evaluate that classifier with respect to an optimal classifier, a Bayes classifier. Here it is shown that for the rules considered an equivalent classifier exists. Also, a brief consideration of meta and meta-meta rules is included. Also, a taxonomy of expert systems is presented and an assertion made that an equivalent classifier exists for each type of expert system in the taxonomy with associated sets of underlying assumptions.

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

PubMed

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Exploring human error in military aviation flight safety events using post-incident classification systems.

PubMed

Hooper, Brionny J; O'Hare, David P A

2013-08-01

Human error classification systems theoretically allow researchers to analyze postaccident data in an objective and consistent manner. The Human Factors Analysis and Classification System (HFACS) framework is one such practical analysis tool that has been widely used to classify human error in aviation. The Cognitive Error Taxonomy (CET) is another. It has been postulated that the focus on interrelationships within HFACS can facilitate the identification of the underlying causes of pilot error. The CET provides increased granularity at the level of unsafe acts. The aim was to analyze the influence of factors at higher organizational levels on the unsafe acts of front-line operators and to compare the errors of fixed-wing and rotary-wing operations. This study analyzed 288 aircraft incidents involving human error from an Australasian military organization occurring between 2001 and 2008. Action errors accounted for almost twice (44%) the proportion of rotary wing compared to fixed wing (23%) incidents. Both classificatory systems showed significant relationships between precursor factors such as the physical environment, mental and physiological states, crew resource management, training and personal readiness, and skill-based, but not decision-based, acts. The CET analysis showed different predisposing factors for different aspects of skill-based behaviors. Skill-based errors in military operations are more prevalent in rotary wing incidents and are related to higher level supervisory processes in the organization. The Cognitive Error Taxonomy provides increased granularity to HFACS analyses of unsafe acts.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.