10 CFR 110.123 - Notice of intent to introduce classified information.

Code of Federal Regulations, 2013 CFR

2013-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When... notice of intent shall be unclassified and, to the extent consistent with classification requirements...

10 CFR 110.123 - Notice of intent to introduce classified information.

Code of Federal Regulations, 2014 CFR

2014-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When... notice of intent shall be unclassified and, to the extent consistent with classification requirements...

10 CFR 110.123 - Notice of intent to introduce classified information.

Code of Federal Regulations, 2010 CFR

2010-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When... notice of intent shall be unclassified and, to the extent consistent with classification requirements...

10 CFR 110.123 - Notice of intent to introduce classified information.

Code of Federal Regulations, 2012 CFR

2012-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When... notice of intent shall be unclassified and, to the extent consistent with classification requirements...

10 CFR 110.123 - Notice of intent to introduce classified information.

Code of Federal Regulations, 2011 CFR

2011-01-01

...; (2) The highest level of classification of the information (confidential, secret or other); (3) When... notice of intent shall be unclassified and, to the extent consistent with classification requirements...

A combined qualitative-quantitative approach for the identification of highly co-creative technology-driven firms

NASA Astrophysics Data System (ADS)

Milyakov, Hristo; Tanev, Stoyan; Ruskov, Petko

2011-03-01

Value co-creation, is an emerging business and innovation paradigm, however, there is not enough clarity on the distinctive characteristics of value co-creation as compared to more traditional value creation approaches. The present paper summarizes the results from an empirically-derived research study focusing on the development of a systematic procedure for the identification of firms that are active in value co-creation. The study is based on a sample 273 firms that were selected for being representative of the breadth of their value co-creation activities. The results include: i) the identification of the key components of value co-creation based on a research methodology using web search and Principal Component Analysis techniques, and ii) the comparison of two different classification techniques identifying the firms with the highest degree of involvement in value co-creation practices. To the best of our knowledge this is the first study using sophisticated data collection techniques to provide a classification of firms according to the degree of their involvement in value co-creation.

Grouping Parturients by Parity, Previous-Cesarean, and Mode of Delivery (P-C-MoD Classification) Better Identifies Groups at Risk for Postpartum Hemorrhage.

PubMed

Reichman, Orna; Gal, Micahel; Sela, Hen Y; Khayyat, Izzat; Emanuel, Michael; Samueloff, Arnon

2016-10-01

Objective We aimed to create a clinical classification to better identify parturients at risk for postpartum hemorrhage (PPH). Method A retrospective cohort, including all women who delivered at a single tertiary care medical center, between 2006 and 2014. Parturients were grouped by parity and history of cesarean delivery (CD): primiparas, multipara, and multipara with previous CD. Each were further subgrouped by mode of delivery (spontaneous vaginal delivery [SVD], operative vaginal delivery [OVD], emergency or elective CD). In all, 12 subgroups, based on parity, previous cesarean, and mode of delivery, formed the P-C-MoD classification. PPH was defined as a decrease of ≥3 gram% hemoglobin from admission and/or transfusion of blood products. Univariate analysis followed by multivariate analysis was performed to assess risk for PPH, controlling for confounders. Results The crude rate of PPH among 126,693 parturients was 7%. The prevalence differed significantly among independent risk factors: primiparity, 14%; multiparity, 4%; OVD, 22%; and CD, 15%. The P-C-MoD classification, segregated better between parturients at risk for PPH. The prevalence of PPH was highest for primiparous undergoing OVD (27%) compared with multiparous with SVD (3%), odds ratio [OR] = 12.8 (95% confidence interval [CI],11.9-13.9). These finding were consistent in the multivariate analysis OR = 13.1 (95% CI,12.1-14.3). Conclusion Employing the P-C-MoD classification more readily identifies parturients at risk for PPH and is superior to estimations based on single risk factors. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

44 CFR 8.2 - Original classification authority.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 44 Emergency Management and Assistance 1 2011-10-01 2011-10-01 false Original classification..., DEPARTMENT OF HOMELAND SECURITY GENERAL NATIONAL SECURITY INFORMATION § 8.2 Original classification authority...)(2), E.O. 12356, the following positions have been delegated ORIGINAL TOP SECRET CLASSIFICATION...

The Rise of Concussions in the Adolescent Population.

PubMed

Zhang, Alan L; Sing, David C; Rugg, Caitlin M; Feeley, Brian T; Senter, Carlin

2016-08-01

Concussion injuries have been highlighted to the American public through media and research. While recent studies have shown increased traumatic brain injuries (TBIs) diagnosed in emergency departments across the United States, no studies have evaluated trends in concussion diagnoses across the general US population in various age groups. To evaluate the current incidence and trends in concussions diagnosed across varying age groups and health care settings in a large cross-sectional population. Descriptive epidemiological study. Administrative health records of 8,828,248 members of a large private-payer insurance group in the United States were queried. Patients diagnosed with concussion from years 2007 through 2014 were stratified by year of diagnosis, age group, sex, classification of concussion, and health care setting of diagnosis (eg, emergency department vs physician's office). Chi-square testing was used for statistical analysis. From a cohort of 8,828,248 patients, 43,884 patients were diagnosed with a concussion. Of these patients, 55% were male and over 32% were in the adolescent age group (10-19 years old). The highest incidence of concussion was seen in patients aged 15 to 19 years (16.5/1000 patients), followed by those aged 10 to 14 years (10.5/1000 patients), 20 to 24 years (5.2/1000 patients), and 5 to 9 years (3.5/1000 patients). Overall, there was a 60% increase in concussion incidence from 2007 to 2014. The largest increases were in the 10- to 14-year (143%) and 15- to 19-year (87%) age groups. Based on International Classification of Disease-9th Revision classification, 29% of concussions were associated with some form of loss of consciousness. Finally, 56% of concussions were diagnosed in the emergency department and 29% in a physician's office, with the remainder in urgent care clinics or inpatient settings. The incidence of concussion diagnosed in the general US population is increasing, driven largely by a substantial rise in the adolescent age group. The youth population should be prioritized for ongoing work in concussion education, diagnosis, treatment, and prevention. The rise of concussions in the adolescent age group across the general population is concerning, and clinical efforts to prevent these injuries are needed.

Exploring the relationship between social class, mental illness stigma and mental health literacy using British national survey data.

PubMed

Holman, Daniel

2015-07-01

The relationship between social class and mental illness stigma has received little attention in recent years. At the same time, the concept of mental health literacy has become an increasingly popular way of framing knowledge and understanding of mental health issues. British Social Attitudes survey data present an opportunity to unpack the relationships between these concepts and social class, an important task given continuing mental health inequalities. Regression analyses were undertaken which centred on depression and schizophrenia vignettes, with an asthma vignette used for comparison. The National Statistics Socio-economic Classification, education and income were used as indicators of class. A number of interesting findings emerged. Overall, class variables showed a stronger relationship with mental health literacy than stigma. The relationship was gendered such that women with higher levels of education, especially those with a degree, had the lowest levels of stigma and highest levels of mental health literacy. Interestingly, class showed more of an association with stigma for the asthma vignette than it did for both the depression and schizophrenia vignettes, suggesting that mental illness stigma needs to be contextualised alongside physical illness stigma. Education emerged as the key indicator of class, followed by the National Statistics Socio-economic Classification, with income effects being marginal. These findings have implications for targeting health promotion campaigns and increasing service use in order to reduce mental health inequalities. © The Author(s) 2014.

Classification of patients with low back-related leg pain: a systematic review.

PubMed

Stynes, Siobhán; Konstantinou, Kika; Dunn, Kate M

2016-05-23

The identification of clinically relevant subgroups of low back pain (LBP) is considered the number one LBP research priority in primary care. One subgroup of LBP patients are those with back related leg pain. Leg pain frequently accompanies LBP and is associated with increased levels of disability and higher health costs than simple low back pain. Distinguishing between different types of low back-related leg pain (LBLP) is important for clinical management and research applications, but there is currently no clear agreement on how to define and identify LBLP due to nerve root involvement. The aim of this systematic review was to identify, describe and appraise papers that classify or subgroup populations with LBLP, and summarise how leg pain due to nerve root involvement is described and diagnosed in the various systems. The search strategy involved nine electronic databases including Medline and Embase, reference lists of eligible studies and relevant reviews. Selected papers were appraised independently by two reviewers using a standardised scoring tool. Of 13,358 initial potential eligible citations, 50 relevant papers were identified that reported on 22 classification systems. Papers were grouped according to purpose and criteria of the classification systems. Five themes emerged: (i) clinical features (ii) pathoanatomy (iii) treatment-based approach (iv) screening tools and prediction rules and (v) pain mechanisms. Three of the twenty two systems focused specifically on LBLP populations. Systems that scored highest following quality appraisal were ones where authors generally included statistical methods to develop their classifications, and supporting work had been published on the systems' validity, reliability and generalisability. There was lack of consistency in how LBLP due to nerve root involvement was described and diagnosed within the systems. Numerous classification systems exist that include patients with leg pain, a minority of them focus specifically on distinguishing between different presentations of leg pain. Further work is needed to identify clinically meaningful subgroups of LBLP patients, ideally based on large primary care cohort populations and using recommended methods for classification system development.

Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.

PubMed

Solti, Imre; Cooke, Colin R; Xia, Fei; Wurfel, Mark M

2009-11-01

This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators.

Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches

PubMed Central

Solti, Imre; Cooke, Colin R.; Xia, Fei; Wurfel, Mark M.

2010-01-01

This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators. PMID:21152268

47 CFR 10.400 - Classification.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 1 2013-10-01 2013-10-01 false Classification. 10.400 Section 10.400 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL WIRELESS EMERGENCY ALERTS Alert Message Requirements § 10.400 Classification. A Participating CMS Provider is required to receive and transmit three classes...

47 CFR 10.400 - Classification.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 1 2014-10-01 2014-10-01 false Classification. 10.400 Section 10.400 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL WIRELESS EMERGENCY ALERTS Alert Message Requirements § 10.400 Classification. A Participating CMS Provider is required to receive and transmit three classes...

40 CFR 51.900 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... higher or lower, classifications are ranked from lowest to highest as follows: classification under... National Ambient Air Quality Standard § 51.900 Definitions. The following definitions apply for purposes of... 42 U.S.C. 7401-7671q (2003). (f) Applicable requirements means for an area the following requirements...

40 CFR 51.900 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... higher or lower, classifications are ranked from lowest to highest as follows: classification under... National Ambient Air Quality Standard § 51.900 Definitions. The following definitions apply for purposes of... 42 U.S.C. 7401-7671q (2003). (f) Applicable requirements means for an area the following requirements...

32 CFR 2001.24 - Additional requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

...,” “Secret,” and “Confidential” shall not be used to identify classified national security information. (b) Transmittal documents. A transmittal document shall indicate on its face the highest classification level of... Removed or Upon Removal of Attachments, This Document is (Classification Level) (c) Foreign government...

Asian Americans: diabetes prevalence across U.S. and World Health Organization weight classifications.

PubMed

Oza-Frank, Reena; Ali, Mohammed K; Vaccarino, Viola; Narayan, K M Venkat

2009-09-01

To compare diabetes prevalence among Asian Americans by World Health Organization and U.S. BMI classifications. Data on Asian American adults (n = 7,414) from the National Health Interview Survey for 1997-2005 were analyzed. Diabetes prevalence was estimated across weight and ethnic group strata. Regardless of BMI classification, Asian Indians and Filipinos had the highest prevalence of overweight (34-47 and 35-47%, respectively, compared with 20-38% in Chinese; P < 0.05). Asian Indians also had the highest ethnic-specific diabetes prevalence (ranging from 6-7% among the normal weight to 19-33% among the obese) compared with non-Hispanic whites: odds ratio (95% CI) for Asian Indians 2.0 (1.5-2.6), adjusted for age and sex, and 3.1 (2.4-4.0) with additional adjustment for BMI. Asian Indian ethnicity, but not other Asian ethnicities, was strongly associated with diabetes. Weight classification as a marker of diabetes risk may need to accommodate differences across Asian subgroups.

Teenager injury panorama in northern Sweden.

PubMed

Johansson, L; Eriksson, A; Björnstig, U

2001-08-01

To study non-fatal unintentional injuries among teenagers and to suggest preventive measures. The emergency care unit of the University Hospital, Umeå, Sweden. All injured teenagers (N = 1044) attending the emergency care unit during 1991 were asked to answer a questionnaire focusing on when, where and how the injury occurred. All available medical records were examined. Data were coded according to the Nordic Medico-Statistical Committees Classification for Accident Monitoring, NOMESCO, and to the Abbreviated Injury Scale, AIS. 1,043 teenagers were treated with sports and transportation related injuries as the most common ones. Most injuries were minor (AIS 1), transportation related injuries had the highest proportion of non-minor injuries (AIS > or = 2), 139 teenagers were admitted for in-patient care. Most injuries occurred during leisure/school time. Sports and transportation related injuries were most frequent. Body weight and length differs among teenagers, we suggest that teenagers should exercise and play together, not only by age, but also to some extent, to height and weight. Curfew laws, a compulsory bicycle helmet law are other injury reducing measures suggested.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Classifying High-noise EEG in Complex Environments for Brain-computer Interaction Technologies

DTIC Science & Technology

2012-02-01

differentiation in the brain signal that our classification approach seeks to identify despite the noise in the recorded EEG signal and the complexity of...performed two offline classifications , one using BCILab (1), the other using LibSVM (2). Distinct classifiers were trained for each individual in...order to improve individual classifier performance (3). The highest classification performance results were obtained using individual frequency bands

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Proposals for Enhancing the Auditory Presentation of Passive Sonar Information

DTIC Science & Technology

1994-11-01

Affecting Auditory Displays. NAVTRAEQUIPCEN 80-D-0011/0037-1, Eagle Technology Incorporated, Orlando FL, 1984. 8. MACMILLAN, N. A. and C. D. CREELMAN ... Psychology , Denver, CO, 1971. -7- P148988.PDF [Page: 14 of 15] SECURITY CLASSIFICATION OF FORM (Highest classification of Title, Abstract, Keywords

Soil Genesis and Development, Lesson 5 - Soil Geography and Classification

USDA-ARS?s Scientific Manuscript database

The system of soil classification developed by the United States Department of Agriculture (USDA) is called Soil Taxonomy. Soil Taxonomy consists of a hierarchy of six levels which, from highest to lowest, are: Order, Suborder, Great Group, Subgroup, family, and series. This lesson will focus on bro...

Method of and system for classifying emergency locating transmitters and emergency positions indicating radio beacons

NASA Technical Reports Server (NTRS)

Wren, Paul E. (Inventor)

1983-01-01

During a distress call, a distress location transmitter 10 generates a high frequency carrier signal 40 that is modulated by a predetermined distress waveform characteristic 29. The classification of user associated with the distress call is identified by periodically interrupting modulation 42; user classification is determined by the repetition rate of the interruptions, the interruption periods, or both.

Supervised classification in the presence of misclassified training data: a Monte Carlo simulation study in the three group case.

PubMed

Bolin, Jocelyn Holden; Finch, W Holmes

2014-01-01

Statistical classification of phenomena into observed groups is very common in the social and behavioral sciences. Statistical classification methods, however, are affected by the characteristics of the data under study. Statistical classification can be further complicated by initial misclassification of the observed groups. The purpose of this study is to investigate the impact of initial training data misclassification on several statistical classification and data mining techniques. Misclassification conditions in the three group case will be simulated and results will be presented in terms of overall as well as subgroup classification accuracy. Results show decreased classification accuracy as sample size, group separation and group size ratio decrease and as misclassification percentage increases with random forests demonstrating the highest accuracy across conditions.

Communication of Children Symptoms in Emergency: Classification of the Terminology.

PubMed

Rochat, Jessica; Siebert, Johan; Galetto, Annick; Lovis, Christian; Ehrler, Frédéric

2017-01-01

The significant part of non-urgent visits to the emergency highlight the necessity to advise people on the actions to take according to their symptoms. Although information sources are accessible through different channels their content often employs medical terminologies that are difficult to understand by laypersons. Our goal is to provide a terminology of the most common symptoms in pediatric emergency adapted to laypersons. This terminology is organized in a hierarchy by the mean of a card-sorting study. The resulting classification separates the symptoms into two main categories: "accident" and "illness" that are subdivided in 9 and 10 sub-categories. The study also revealed that some symptoms were not understood by the participants and had to be reformulated, confirming the importance of user-centered method. The classification resulting from this study will be evaluated through a tree-test.

40 CFR 52.321 - Classification of regions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 3 2011-07-01 2011-07-01 false Classification of regions. 52.321 Section 52.321 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) APPROVAL AND PROMULGATION OF IMPLEMENTATION PLANS Colorado § 52.321 Classification of regions. The revised Denver Emergency Episode Plan, adopte...

40 CFR 52.321 - Classification of regions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 3 2010-07-01 2010-07-01 false Classification of regions. 52.321 Section 52.321 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) APPROVAL AND PROMULGATION OF IMPLEMENTATION PLANS Colorado § 52.321 Classification of regions. The revised Denver Emergency Episode Plan, adopte...

[An investigation on job burnout of medical personnel in a top three hospital].

PubMed

Li, Y Y; Li, L P

2016-05-20

To investigate job burnout status of medical Personnel in a top three hospitals, in order to provide basic data for intervention of the hospital management. A total of 549 doctors and nurses were assessed by Maslach Burnout Inventory-Human Service Survey (MBI-HSS). SPSS 19.0 software package was applied to data description and analysis, including univariate analysis and orderly classification Logistic regression analysis. The rate of high job burnout of doctors and nurses are 36.3% and 42.8% respectively. Female subjects got higher scores (29.4±13.5) on emotional exhaustion than male subjects (26.2±12.8) compared with.Doctors got lower scores (28.2±15.9) on emotional exhaustion and higher scores (31.4±9.3) on personal accomplishment than nurses.Compared with subjects with higher professional title, young subjects with primary professional title got lower scores on personal accomplishment.Subjects with 11-20 years working age got the highest scores on depersonalization.Among all the test departments, medical personnel of emergency department got the highest scores (31.9±12.6) on emotional exhaustion,while the lowest scores (28.1±8.0) on personal accomplishment. According to the results of orderly classification Logistic regression analysis, age, job type,professional qualifications and clinical departments type entered the regression model. Physical resources and emotional resources of medical personnel are overdraft so that they got some high degree of job burnout.Much more attention should be paid to professional mental health of nurses,and personnel who at low age,got low professional titles.Positive measures should be provided, including management mechanism,organizational culture, occupational protection and psychological intervention.

A Review of Equation of State Models, Chemical Equilibrium Calculations and CERV Code Requirements for SHS Detonation Modelling

DTIC Science & Technology

2009-10-01

parameters for a large number of species. These authors provide many sample calculations with the JCZS database incorporated in CHEETAH 2.0, including...FORM (highest classification of Title, Abstract, Keywords) DOCUMENT CONTROL DATA (Security classification of title, body of abstract and...CLASSIFICATION OF FORM 13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself

Examination of Aggression and Self Injury in Children with Autism Spectrum Disorders and Serious Behavioral Problems

PubMed Central

Carroll, Devon; Hallett, Victoria; McDougle, Christopher J.; Aman, Michael G.; McCracken, James T.; Tierney, Elaine; Arnold, L. Eugene; Sukhodolsky, Denis G.; Lecavalier, Luc; Handen, Benjamin; Swiezy, Naomi; Johnson, Cynthia; Bearss, Karen; Vitiello, Benedetto; Scahill, Lawrence

2014-01-01

Synopsis This study identified subtypes of aggression in a sample of 206 children (174 boys, 32 girls) with autism spectrum disorder (ASD) who participated in two risperidone trials conducted by the Research Units on Pediatric Psychopharmacology (RUPP) Autism Network. The classification of aggression subtypes was based on a review of brief narratives documented at baseline. The narratives were derived from a parent interview about the child’s two most pressing problems. Five subtypes of aggression emerged: hot aggression only, cold aggression only, self-injurious behavior (SIB) only, aggression and SIB, and non-aggressive. The aggression and SIB group had the highest proportion of children with IQ below 70. Children in the hot aggression group were slightly younger and had higher scores on the ABC-Irritability subscale than the non-aggression group. The SIB only group had the highest ABC-Irritability score. All groups showed a high rate of positive response to risperidone with no differences across subtypes. These study findings extend our understanding of aggression in ASD and may be useful to guide further study on biological mechanisms and individualized treatment in ASD. PMID:24231167

Exploiting the systematic review protocol for classification of medical abstracts.

PubMed

Frunza, Oana; Inkpen, Diana; Matwin, Stan; Klement, William; O'Blenis, Peter

2011-01-01

To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers. The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement naïve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload. For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%. The per-question method that combines classifiers following the specific protocol of the review leads to better results than the global method in terms of recall. Because neither method is efficient enough to classify abstracts reliably by itself, the technology should be applied in a semi-automatic way, with a human expert still involved. When the workflow includes one human expert and the trained automatic classifier, recall improves to an acceptable level, showing that automatic classification techniques can reduce the human workload in the process of building a systematic review. Copyright © 2010 Elsevier B.V. All rights reserved.

Global cardiac risk assessment in the Registry Of Pregnancy And Cardiac disease: results of a registry from the European Society of Cardiology.

PubMed

van Hagen, Iris M; Boersma, Eric; Johnson, Mark R; Thorne, Sara A; Parsonage, William A; Escribano Subías, Pilar; Leśniak-Sobelga, Agata; Irtyuga, Olga; Sorour, Khaled A; Taha, Nasser; Maggioni, Aldo P; Hall, Roger; Roos-Hesselink, Jolien W

2016-05-01

To validate the modified World Health Organization (mWHO) risk classification in advanced and emerging countries, and to identify additional risk factors for cardiac events during pregnancy. The ongoing prospective worldwide Registry Of Pregnancy And Cardiac disease (ROPAC) included 2742 pregnant women (mean age ± standard deviation, 29.2 ± 5.5 years) with established cardiac disease: 1827 from advanced countries and 915 from emerging countries. In patients from advanced countries, congenital heart disease was the most prevalent diagnosis (70%) while in emerging countries valvular heart disease was more common (55%). A cardiac event occurred in 566 patients (20.6%) during pregnancy: 234 (12.8%) in advanced countries and 332 (36.3%) in emerging countries. The mWHO classification had a moderate performance to discriminate between women with and without cardiac events (c-statistic 0.711 and 95% confidence interval (CI) 0.686-0.735). However, its performance in advanced countries (0.726) was better than in emerging countries (0.633). The best performance was found in patients with acquired heart disease from developed countries (0.712). Pre-pregnancy signs of heart failure and, in advanced countries, atrial fibrillation and no previous cardiac intervention added prognostic value to the mWHO classification, with a c-statistic of 0.751 (95% CI 0.715-0.786) in advanced countries and of 0.724 (95% CI 0.691-0.758) in emerging countries. The mWHO risk classification is a useful tool for predicting cardiac events during pregnancy in women with established cardiac disease in advanced countries, but seems less effective in emerging countries. Data on pre-pregnancy cardiac condition including signs of heart failure and atrial fibrillation, may help to improve preconception counselling in advanced and emerging countries. © 2016 The Authors. European Journal of Heart Failure © 2016 European Society of Cardiology.

Comparison of dual-biomarker PIB-PET and dual-tracer PET in AD diagnosis.

PubMed

Fu, Liping; Liu, Linwen; Zhang, Jinming; Xu, Baixuan; Fan, Yong; Tian, Jiahe

2014-11-01

To identify the optimal time window for capturing perfusion information from early (11)C-PIB imaging frames (perfusion PIB, (11)C-pPIB) and to compare the performance of (18)F-FDG PET and "dual biomarker" (11)C-PIB PET [(11)C-pPIB and amyloid PIB ((11)C-aPIB)] for classification of AD, MCI and CN subjects. Forty subjects (14 CN, 12 MCI and 14 AD patients) underwent (18)F-FDG and (11)C-PIB PET studies. Pearson correlation between the (18)F-FDG image and sum of early (11)C-PIB frames was maximised to identify the optimal time window for (11)C-pPIB. The classification power of imaging parameters was evaluated with a leave-one-out validation. A 7-min time window yielded the highest correlation between (18)F-FDG and (11)C-pPIB. (11)C-pPIB and (18)F-FDG images shared a similar radioactive distribution pattern. (18)F-FDG performed better than (11)C-pPIB for the classification of both AD vs. CN and MCI vs. CN. (11)C-pPIB + (11)C-aPIB and (18)F-FDG + (11)C-aPIB yielded the highest classification accuracy for the classification of AD vs. CN, and (18)F-FDG + (11)C-aPIB had the best classification performance for the classification of MCI vs. C-pPIB could serve as a useful biomarker of rCBF for measuring neural activity and improve the diagnostic power of PET for AD in conjunction with (11)C-aPIB. (18)F-FDG and (11)C-PIB dual-tracer PET examination could better detect MCI. • Dual-tracer PET examination provides neurofunctional and neuropathological information for AD diagnosis. • The identified optimal 11C-pPIB time frames had highest correlation with 18F-FDG. • 11C-pPIB images shared a similar radioactive distribution pattern with 18F-FDG images. • 11C-pPIB can provide neurofunctional information. • Dual-tracer PET examination could better detect MCI.

Geomorphic Classification and Evaluation of Channel Width and Emergent Sandbar Habitat Relations on the Lower Platte River, Nebraska

USGS Publications Warehouse

Elliott, Caroline M.

2011-01-01

This report presents a summary of geomorphic characteristics extracted from aerial imagery for three broad segments of the Lower Platte River. This report includes a summary of the longitudinal multivariate classification in Elliott and others (2009) and presents a new analysis of total channel width and habitat variables. Three segments on the lower 102.8 miles of the Lower Platte River are addressed in this report: the Loup River to the Elkhorn River (70 miles long), the Elkhorn River to Salt Creek (6.9 miles long), and Salt Creek to the Missouri River (25.9 miles long). The locations of these segments were determined by the locations of tributaries potentially significant to the hydrology or sediment supply of the Lower Platte River. This report summarizes channel characteristics as mapped from July 2006 aerial imagery including river width, valley width, channel curvature, and in-channel habitat features. In-channel habitat measurements were not made under consistent hydrologic conditions and must be considered general estimates of channel condition in late July 2006. Longitudinal patterns in these features are explored and are summarized in the context of the longitudinal multivariate classification in Elliott and others (2009) for the three Lower Platte River segments. Detailed descriptions of data collection and classification methods are described in Elliott and others (2009). Nesting data for the endangered interior least tern (Sternula antillarum) and threatened piping plover (Charadrius melodus) from 2006 through 2009 are examined within the context of the multivariate classification and Lower Platte River segments. The widest reaches of the Lower Platte River are located in the segment downstream from the Loup River to the Elkhorn River. This segment also has the widest valley and highest degree of braiding of the three segments and many large vegetated islands. The short segment of river between the Elkhorn River and Salt Creek has a fairly low valley width and high channel sinuosities at larger scales. The segment from Salt Creek to the Missouri River has narrow valleys and generally low channel sinuosity. Tern and plover nest sites from 2006 through 2009 in the multi-scale multivariate classification indicated relative nesting selection of cluster 2 reaches among the four-cluster classification and reaches containing clusters 2, 3, and 6 from the seven-cluster classification. These classes, with the exception of cluster 6 are common downstream from the Elkhorn River. Trends in total channel width indicated that reaches dominated by dark vegetation (islands) are the widest on the Lower Platte River. Reaches with high percentages of dry sand and dry sand plus light vegetation were the narrowest reaches. This suggests that narrow channel reaches have sufficient transport capacity to maintain sandbars under recent (2006) flow regimes and are likely to be most amenable to maintaining tern and plover habitat in the Lower Platte River. Further investigations into the dynamics of emergent sandbar habitat and the effects of bank stabilization on in-channel habitats will require the collection and analysis of new data, particularly detailed elevation information and an assessment of existing bank stabilization structures.

Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales.

PubMed

Oh, Jihoon; Yun, Kyongsik; Hwang, Ji-Hyun; Chae, Jeong-Ho

2017-01-01

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders ( N  = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings.

Characteristics of Illinois Public Community College Faculty and Staff, Fall Term 1984.

ERIC Educational Resources Information Center

Illinois Community Coll. Board, Springfield.

Data on Illinois community college faculty and staff characteristics are presented and analyzed in this report for fall 1984. Tables provide statistics on faculty and staff employment classification by college; full-time employment classification by sex and ethnic origin; full-time teaching faculty by highest degree held, age, sex, tenure status,…

10 CFR 2.908 - Contents of notice of intent to introduce restricted data or other national security information.

Code of Federal Regulations, 2014 CFR

2014-01-01

... notice shall be unclassified and, to the extent consistent with classification requirements, shall... Information which it is anticipated will be involved; (2) The highest level of classification of the information (confidential, secret, or other); (3) The stage of the proceeding at which he anticipates a need...

10 CFR 2.908 - Contents of notice of intent to introduce restricted data or other national security information.

Code of Federal Regulations, 2013 CFR

2013-01-01

... notice shall be unclassified and, to the extent consistent with classification requirements, shall... Information which it is anticipated will be involved; (2) The highest level of classification of the information (confidential, secret, or other); (3) The stage of the proceeding at which he anticipates a need...

Landscape object-based analysis of wetland plant functional types: the effects of spatial scale, vegetation classes and classifier methods

NASA Astrophysics Data System (ADS)

Dronova, I.; Gong, P.; Wang, L.; Clinton, N.; Fu, W.; Qi, S.

2011-12-01

Remote sensing-based vegetation classifications representing plant function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new classification tools; however, few comparisons of different algorithms and spatial scales have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from 30-m Landsat TM scene at the peak of spring growing season. We targeted major PFTs (C3 grasses, C3 forbs and different types of C4 grasses and aquatic vegetation) that are both key players in system's biogeochemical cycles and critical providers of waterbird habitat. Classification results were compared among: a) several object segmentation scales (with average object sizes 900-9000 m2); b) several families of statistical classifiers (including Bayesian, Logistic, Neural Network, Decision Trees and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and more detailed 6-class set. We found that classification benefited from object-based approach which allowed including object shape, texture and context descriptors in classification. While a number of classifiers achieved high accuracy at the finest pixel-equivalent segmentation scale, the highest accuracies and best agreement among algorithms occurred at coarser object scales. No single classifier was consistently superior across all scales, although selected algorithms of Neural Network, Logistic and K-Nearest Neighbors families frequently provided the best discrimination of classes at different scales. The choice of vegetation categories also affected classification accuracy. The 6-class set allowed for higher individual class accuracies but lower overall accuracies than the 3-class set because individual classes differed in scales at which they were best discriminated from others. Main classification challenges included a) presence of C3 grasses in C4-grass areas, particularly following harvesting of C4 reeds and b) mixtures of emergent, floating and submerged aquatic plants at sub-object and sub-pixel scales. We conclude that OBIA with advanced statistical classifiers offers useful instruments for landscape vegetation analyses, and that spatial scale considerations are critical in mapping PFTs, while multi-scale comparisons can be used to guide class selection. Future work will further apply fuzzy classification and field-collected spectral data for PFT analysis and compare results with MODIS PFT products.

Documenting Community Engagement Practices and Outcomes: Insights from Recipients of the 2010 Carnegie Community Engagement Classification

ERIC Educational Resources Information Center

Noel, Jana; Earwicker, David P.

2015-01-01

This study was performed to document the strategies and methods used by successful applicants for the 2010 Carnegie Community Engagement Classification and to document the cultural shifts connected with the application process and receipt of the Classification. Four major findings emerged: (1) Applicants benefited from a team approach; (2)…

Indications and limitations of chemotherapy and targeted agents in non-small cell lung cancer brain metastases.

PubMed

Zimmermann, Stefan; Dziadziuszko, Rafal; Peters, Solange

2014-07-01

Lung cancer is characterized by the highest incidence of solid tumor-related brain metastases, which are reported with a growing incidence during the last decade. Prognostic assessment may help to identify subgroups of patients that could benefit from more aggressive therapy of metastatic disease, in particular when central nervous system is involved. The recent sub-classification of non-small cell lung cancer (NSCLC) into molecularly-defined "oncogene-addicted" tumors, the emergence of effective targeted treatments in molecularly defined patient subsets, global improvement of advanced NSCLC survival as well as the availability of refined new radiotherapy techniques are likely to impact on outcomes of patients with brain dissemination. The present review focuses on key evidence and research strategies for systemic treatment of patients with central nervous system involvement in non-small cell lung cancer. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

21 CFR 868.6175 - Cardiopulmonary emergency cart.

Code of Federal Regulations, 2011 CFR

2011-04-01

... cardiopulmonary resuscitation. (b) Classification. Class I (general controls). The device is exempt from the... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Cardiopulmonary emergency cart. 868.6175 Section... (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Miscellaneous § 868.6175 Cardiopulmonary emergency cart...

21 CFR 868.6175 - Cardiopulmonary emergency cart.

Code of Federal Regulations, 2010 CFR

2010-04-01

... cardiopulmonary resuscitation. (b) Classification. Class I (general controls). The device is exempt from the... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Cardiopulmonary emergency cart. 868.6175 Section... (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Miscellaneous § 868.6175 Cardiopulmonary emergency cart...

21 CFR 868.6175 - Cardiopulmonary emergency cart.

Code of Federal Regulations, 2014 CFR

2014-04-01

... cardiopulmonary resuscitation. (b) Classification. Class I (general controls). The device is exempt from the... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Cardiopulmonary emergency cart. 868.6175 Section... (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Miscellaneous § 868.6175 Cardiopulmonary emergency cart...

21 CFR 868.6175 - Cardiopulmonary emergency cart.

Code of Federal Regulations, 2012 CFR

2012-04-01

... cardiopulmonary resuscitation. (b) Classification. Class I (general controls). The device is exempt from the... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Cardiopulmonary emergency cart. 868.6175 Section... (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Miscellaneous § 868.6175 Cardiopulmonary emergency cart...

21 CFR 868.6175 - Cardiopulmonary emergency cart.

Code of Federal Regulations, 2013 CFR

2013-04-01

... cardiopulmonary resuscitation. (b) Classification. Class I (general controls). The device is exempt from the... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Cardiopulmonary emergency cart. 868.6175 Section... (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Miscellaneous § 868.6175 Cardiopulmonary emergency cart...

44 CFR 10.9 - Preparation of environmental assessments.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Preparation of environmental assessments. 10.9 Section 10.9 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY... quickly; (4) Likelihood of meaningful public comment; (5) National security classification issues; (6...

Radiological Evaluation of Strategic Structures in Patients with Mild Cognitive Impairment and Early Alzheimer's Disease.

PubMed

Nesteruk, Tomasz; Nesteruk, Marta; Styczyńska, Maria; Barcikowska-Kotowicz, Maria; Walecki, Jerzy

2016-01-01

The aim of the study was to evaluate the diagnostic value of two measurement techniques in patients with cognitive impairment - automated volumetry of the hippocampus, entorhinal cortex, parahippocampal gyrus, posterior cingulate gyrus, cortex of the temporal lobes and corpus callosum, and fractional anisotropy (FA) index measurement of the corpus callosum using diffusion tensor imaging. A total number of 96 patients underwent magnetic resonance imaging study of the brain - 33 healthy controls (HC), 33 patients with diagnosed mild cognitive impairment (MCI) and 30 patients with Alzheimer's disease (AD) in early stage. The severity of the dementia was evaluated with neuropsychological test battery. The volumetric measurements were performed automatically using FreeSurfer imaging software. The measurements of FA index were performed manually using ROI (region of interest) tool. The volumetric measurement of the temporal lobe cortex had the highest correct classification rate (68.7%), whereas the lowest was achieved with FA index measurement of the corpus callosum (51%). The highest sensitivity and specificity in discriminating between the patients with MCI vs. early AD was achieved with the volumetric measurement of the corpus callosum - the values were 73% and 71%, respectively, and the correct classification rate was 72%. The highest sensitivity and specificity in discriminating between HC and the patients with early AD was achieved with the volumetric measurement of the entorhinal cortex - the values were 94% and 100%, respectively, and the correct classification rate was 97%. The highest sensitivity and specificity in discriminating between HC and the patients with MCI was achieved with the volumetric measurement of the temporal lobe cortex - the values were 90% and 93%, respectively, and the correct classification rate was 92%. The diagnostic value varied depending on the measurement technique. The volumetric measurement of the atrophy proved to be the best imaging biomarker, which allowed the distinction between the groups of patients. The volumetric assessment of the corpus callosum proved to be a useful tool in discriminating between the patients with MCI vs. early AD.

Should there be a "Wet" Soil Order in Soil Taxonomy?

NASA Astrophysics Data System (ADS)

Rabenhorst, Martin; Wessel, Barret; Stolt, Mark; Lindbo, David

2017-04-01

Early soil classification systems recognized wet soils at the highest categorical level. Among the Intrazonal Soils of the US classification utilized between the 1920s and 1960, were included as Great Soil Groups, the Wiesenboden, Bog, Half-Bog, Ground-Water Podzols and Ground-Water Laterites. In other systems, groups named with such terms as ground water gley and pseudogley were also used. With the advent of Soil Taxonomy and it's precursor (1960, 1975), Histosols (organic soils) were distinguished as one of the initial 10 soil orders, and while many of these organic soils are wet soils, some are not (Folists for example). Thus, for over 50 years, with the exception of Histosols, wet soils (which typically represent the wettest end of subaerial wet soils) have not been collectively recognized within taxa at the highest categorical level (order) in the US soil classification system. Rather, the wettest soils were designated at the second categorical level as wet (Aqu) suborders among the various soil orders, and more recently, subaqueous soils as "Wass" suborders of Entisols and Histosols. Soils with less-wet conditions have been recognized at the subgroup (4th) level. Further, in impoundments and regions of transgressing coastlines, submerged upland soils have been found that still classify in soil orders that do not accommodate subaqueous soils ("Wass" suborders). Notwithstanding, other contemporary soil classification systems do (have continued to) recognize wet soils at the highest level. In the World Reference Base (WRB) for example, wet soils are designated as Gleysols or Stagnosols. As efforts are underway to revisit, simplify, and revise Soil Taxonomy, questions have been raised regarding whether wet soils should again be moved back with a place among taxa at the highest category using a name such as Hydrasols, Aquasols, etc. This paper will explore and consider the questions and arguments for and against such proposals and the difficult question regarding where along the soil wetness continuum would be the best point for recognizing a wet soil order.

The "prudent layperson" definition of an emergency medical condition.

PubMed

Li, James; Galvin, Hannah K; Johnson, Sandra C

2002-01-01

The study objectives, based on federal and state legislative language, were to objectively define symptoms and signs commonly agreed on by "prudent laypersons" as "emergency medical conditions." After comprehensive tabulation of symptom classifications from the International Classification of Diseases (ICD-9), we performed a survey of nonmedical laypersons. Data analysis included descriptive statistics, proportional calculations, and 95% confidence intervals. A minority of symptoms and signs (25/87, 29%) were considered emergency medical conditions by more than half of nonmedical survey respondents who were self-defined as prudent laypersons. The leading conditions deemed emergencies were loss of consciousness, seizure, no recognition of one side of the body, paralysis, shock, gangrene, coughing blood, trouble breathing, chest pain, and choking. Pain, except for renal colic or chest pain, was not considered an emergency. No symptoms or signs specifically related to gynecologic disorders were considered emergencies. Most symptoms and signs tabulated in the diagnostic coding manual, ICD-9, are not considered emergency medical conditions by self-designated prudent laypersons. These include many conditions that are commonly investigated and treated in the emergency department setting. Use of the prudent layperson standard for reimbursable emergency health services may not reflect the actual scope of symptoms necessitating emergency care.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Accuracy of Remotely Sensed Classifications For Stratification of Forest and Nonforest Lands

Treesearch

Raymond L. Czaplewski; Paul L. Patterson

2001-01-01

We specify accuracy standards for remotely sensed classifications used by FIA to stratify landscapes into two categories: forest and nonforest. Accuracy must be highest when forest area approaches 100 percent of the landscape. If forest area is rare in a landscape, then accuracy in the nonforest stratum must be very high, even at the expense of accuracy in the forest...

The effect of medical and operative birth interventions on child health outcomes in the first 28 days and up to 5 years of age: A linked data population-based cohort study.

PubMed

Peters, Lilian L; Thornton, Charlene; de Jonge, Ank; Khashan, Ali; Tracy, Mark; Downe, Soo; Feijen-de Jong, Esther I; Dahlen, Hannah G

2018-03-25

Spontaneous vaginal birth rates are decreasing worldwide, while cesarean delivery, instrumental births, and medical birth interventions are increasing. Emerging evidence suggests that birth interventions may have an effect on children's health. Therefore, the aim of our study was to examine the association between operative and medical birth interventions on the child's health during the first 28 days and up to 5 years of age. In New South Wales (Australia), population-linked data sets were analyzed, including data on maternal characteristics, child characteristics, mode of birth, interventions during labor and birth, and adverse health outcomes of the children (ie, jaundice, feeding problems, hypothermia, asthma, respiratory infections, gastrointestinal disorders, other infections, metabolic disorder, and eczema) registered with the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification codes. Logistic regression analyses were performed for each adverse health outcome. Our analyses included 491 590 women and their children; of those 38% experienced a spontaneous vaginal birth. Infants who experienced an instrumental birth after induction or augmentation had the highest risk of jaundice, adjusted odds ratio (aOR) 2.75 (95% confidence interval [CI] 2.61-2.91) compared with spontaneous vaginal birth. Children born by cesarean delivery were particularly at statistically significantly increased risk for infections, eczema, and metabolic disorder, compared with spontaneous vaginal birth. Children born by emergency cesarean delivery showed the highest association for metabolic disorder, aOR 2.63 (95% CI 2.26-3.07). Children born by spontaneous vaginal birth had fewer short- and longer-term health problems, compared with those born after birth interventions. © 2018 the Authors. Birth published by Wiley Periodicals, Inc.

Classification of CO2 Geologic Storage: Resource and Capacity

USGS Publications Warehouse

Frailey, S.M.; Finley, R.J.

2009-01-01

The use of the term capacity to describe possible geologic storage implies a realistic or likely volume of CO2 to be sequestered. Poor data quantity and quality may lead to very high uncertainty in the storage estimate. Use of the term "storage resource" alleviates the implied certainty of the term "storage capacity". This is especially important to non- scientists (e.g. policy makers) because "capacity" is commonly used to describe the very specific and more certain quantities such as volume of a gas tank or a hotel's overnight guest limit. Resource is a term used in the classification of oil and gas accumulations to infer lesser certainty in the commercial production of oil and gas. Likewise for CO2 sequestration, a suspected porous and permeable zone can be classified as a resource, but capacity can only be estimated after a well is drilled into the formation and a relatively higher degree of economic and regulatory certainty is established. Storage capacity estimates are lower risk or higher certainty compared to storage resource estimates. In the oil and gas industry, prospective resource and contingent resource are used for estimates with less data and certainty. Oil and gas reserves are classified as Proved and Unproved, and by analogy, capacity can be classified similarly. The highest degree of certainty for an oil or gas accumulation is Proved, Developed Producing (PDP) Reserves. For CO2 sequestration this could be Proved Developed Injecting (PDI) Capacity. A geologic sequestration storage classification system is developed by analogy to that used by the oil and gas industry. When a CO2 sequestration industry emerges, storage resource and capacity estimates will be considered a company asset and consequently regulated by the Securities and Exchange Commission. Additionally, storage accounting and auditing protocols will be required to confirm projected storage estimates and assignment of credits from actual injection. An example illustrates the use of these terms and how storage classification changes as new data become available. ?? 2009 Elsevier Ltd. All rights reserved.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Bath salts and synthetic cathinones: An emerging designer drug phenomenon

PubMed Central

German, Christopher L.; Fleckenstein, Annette E.; Hanson, Glen R.

2014-01-01

The synthetic cathinones are an emerging class of designer drugs abused for psychostimulant and hallucinogenic effects similar to cocaine, methylenedioxymethamphetamine (MDMA), or other amphetamines. Abuse of synthetic cathinones, frequently included in products sold as ‘bath salts’, became prevalent in early 2009, leading to legislative classification throughout Europe in 2010 and schedule I classification within the United States in 2011. Recent pre-clinical and clinical studies indicate dysregulation of central monoamine systems are a principal mechanism of synthetic cathinone action and presumably underlie the behavioral effects and abuse liability associated with these drugs. This review provides insight into the development of synthetic cathinones as substances of abuse, current patterns of their abuse, known mechanisms of their action and toxicology, and the benefits and drawbacks of their classification. PMID:23911668

40 CFR 51.150 - Classification of regions for episode plans.

Code of Federal Regulations, 2010 CFR

2010-07-01

... plans. 51.150 Section 51.150 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF IMPLEMENTATION PLANS Prevention of Air Pollution Emergency Episodes § 51.150 Classification of regions for episode plans. (a) This section...

Policy implications of emerging vehicle and infrastructure technology.

DOT National Transportation Integrated Search

2014-08-01

This report considers a broad range of emerging transportation technologies that have potential : for enhancing travel on and operations of the Texas transportation system. It provides an : overview of technology classifications and assesses the poli...

Developing a New Zealand casemix classification for mental health services.

PubMed

Eagar, Kathy; Gaines, Phillipa; Burgess, Philip; Green, Janette; Bower, Alison; Buckingham, Bill; Mellsop, Graham

2004-10-01

This study aimed to develop a casemix classification of characteristics of New Zealand mental health services users. Over a six month period, patient information, staff time and service costs were collected from 8 district health boards. This information was analysed seeking the classification of service user characteristics which best predicted the cost drivers of the services provided. A classification emerged which explained more than two thirds of the variance in service user costs. It can be used to inform service management and funding, but it is premature to have it determine funding.

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.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Compensation for Asbestos-Related Diseases in Japan: Utilization of Standard Classifications of Industry and Occupations

PubMed

Sawanyawisuth, Kittisak; Furuya, Sugio; Park, Eun-Kee; Myong, Jun-Pyo; Ramos-Bonilla, Juan Pablo; Chimed Ochir, Odgerel; Takahashi, Ken

2017-07-27

Background: Asbestos-related diseases (ARD) are occupational hazards with high mortality rates. To identify asbestos exposure by previous occupation is the main issue for ARD compensation for workers. This study aimed to identify risk groups by applying standard classifications of industries and occupations to a national database of compensated ARD victims in Japan. Methods: We identified occupations that carry a risk of asbestos exposure according to the International Standard Industrial Classification of All Economic Activities (ISIC). ARD compensation data from Japan between 2006 and 2013 were retrieved. Each compensated worker was classified by job section and group according to the ISIC code. Risk ratios for compensation were calculated according to the percentage of workers compensated because of ARD in each ISIC category. Results: In total, there were 6,916 workers with ARD who received compensation in Japan between 2008 and 2013. ISIC classification section F (construction) had the highest compensated risk ratio of 6.3. Section C (manufacturing) and section F (construction) had the largest number of compensated workers (2,868 and 3,463, respectively). In the manufacturing section C, 9 out of 13 divisions had a risk ratio of more than 1. For ISIC divisions in the construction section, construction of buildings (division 41) had the highest number of workers registering claims (2,504). Conclusion: ISIC classification of occupations that are at risk of developing ARD can be used to identify the actual risk of workers’ compensation at the national level. Creative Commons Attribution License

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Improving risk classification of critical illness with biomarkers: a simulation study

PubMed Central

Seymour, Christopher W.; Cooke, Colin R.; Wang, Zheyu; Kerr, Kathleen F.; Yealy, Donald M.; Angus, Derek C.; Rea, Thomas D.; Kahn, Jeremy M.; Pepe, Margaret S.

2012-01-01

Purpose Optimal triage of patients at risk of critical illness requires accurate risk prediction, yet little data exists on the performance criteria required of a potential biomarker to be clinically useful. Materials and Methods We studied an adult cohort of non-arrest, non-trauma emergency medical services encounters transported to a hospital from 2002–2006. We simulated hypothetical biomarkers increasingly associated with critical illness during hospitalization, and determined the biomarker strength and sample size necessary to improve risk classification beyond a best clinical model. Results Of 57,647 encounters, 3,121 (5.4%) were hospitalized with critical illness and 54,526 (94.6%) without critical illness. The addition of a moderate strength biomarker (odds ratio=3.0 for critical illness) to a clinical model improved discrimination (c-statistic 0.85 vs. 0.8, p<0.01), reclassification (net reclassification improvement=0.15, 95%CI: 0.13,0.18), and increased the proportion of cases in the highest risk categoryby+8.6% (95%CI: 7.5,10.8%). Introducing correlation between the biomarker and physiological variables in the clinical risk score did not modify the results. Statistically significant changes in net reclassification required a sample size of at least 1000 subjects. Conclusions Clinical models for triage of critical illness could be significantly improved by incorporating biomarkers, yet, substantial sample sizes and biomarker strength may be required. PMID:23566734

40 CFR 164.123 - Emergency order.

Code of Federal Regulations, 2010 CFR

2010-07-01

....123 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Hearings § 164.123 Emergency order. (a) Whenever the Environmental Appeals Board determines that an...

40 CFR 164.123 - Emergency order.

Code of Federal Regulations, 2011 CFR

2011-07-01

....123 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) PESTICIDE PROGRAMS RULES OF... REFUSALS TO REGISTER, CANCELLATIONS OF REGISTRATIONS, CHANGES OF CLASSIFICATIONS, SUSPENSIONS OF... Hearings § 164.123 Emergency order. (a) Whenever the Environmental Appeals Board determines that an...

Classification of hospital admissions into emergency and elective care: a machine learning approach.

PubMed

Krämer, Jonas; Schreyögg, Jonas; Busse, Reinhard

2017-11-25

Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient's primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

44 CFR 329.3 - Procedures.

Code of Federal Regulations, 2010 CFR

2010-10-01

....3 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS USE OF PRIORITIES AND ALLOCATION AUTHORITY FOR FEDERAL SUPPLY CLASSIFICATION (FSC... necessary or appropriate to promote the national defense and why defense-related requirements cannot be met...

44 CFR 329.3 - Procedures.

Code of Federal Regulations, 2013 CFR

2013-10-01

....3 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS USE OF PRIORITIES AND ALLOCATION AUTHORITY FOR FEDERAL SUPPLY CLASSIFICATION (FSC... necessary or appropriate to promote the national defense and why defense-related requirements cannot be met...

44 CFR 329.3 - Procedures.

Code of Federal Regulations, 2014 CFR

2014-10-01

....3 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS USE OF PRIORITIES AND ALLOCATION AUTHORITY FOR FEDERAL SUPPLY CLASSIFICATION (FSC... necessary or appropriate to promote the national defense and why defense-related requirements cannot be met...

44 CFR 329.3 - Procedures.

Code of Federal Regulations, 2011 CFR

2011-10-01

....3 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS USE OF PRIORITIES AND ALLOCATION AUTHORITY FOR FEDERAL SUPPLY CLASSIFICATION (FSC... necessary or appropriate to promote the national defense and why defense-related requirements cannot be met...

44 CFR 329.3 - Procedures.

Code of Federal Regulations, 2012 CFR

2012-10-01

....3 Emergency Management and Assistance FEDERAL EMERGENCY MANAGEMENT AGENCY, DEPARTMENT OF HOMELAND SECURITY PREPAREDNESS USE OF PRIORITIES AND ALLOCATION AUTHORITY FOR FEDERAL SUPPLY CLASSIFICATION (FSC... necessary or appropriate to promote the national defense and why defense-related requirements cannot be met...

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

The Molecular Pathology of Myelodysplastic Syndrome.

PubMed

Haferlach, Torsten

2018-05-23

The diagnosis and classification of myelodysplastic syndromes (MDS) are based on cytomorphology and cytogenetics (WHO classification). Prognosis is best defined by the Revised International Prognostic Scoring System (IPSS-R). In recent years, an increasing number of molecular aberrations have been discovered. They are already included in the classification (e.g., SF3B1) and, more importantly, have emerged as valuable markers for better classification, particularly for defining risk groups. Mutations in genes such as SF3B1 and IDH1/2 have already had an impact on targeted treatment approaches in MDS. © 2018 S. Karger AG, Basel.

Developing a New Zealand casemix classification for mental health services

PubMed Central

Eagar, Kathy; Gaines, Phillipa; Burgess, Philip; Green, Janette; Bower, Alison; Buckingham, Bill; Mellsop, Graham

2004-01-01

This study aimed to develop a casemix classification of characteristics of New Zealand mental health services users. Over a six month period, patient information, staff time and service costs were collected from 8 district health boards. This information was analysed seeking the classification of service user characteristics which best predicted the cost drivers of the services provided. A classification emerged which explained more than two thirds of the variance in service user costs. It can be used to inform service management and funding, but it is premature to have it determine funding. PMID:16633490

Comparability of a short food frequency questionnaire to assess diet quality: the DISCOVER study.

PubMed

Dehghan, Mahshid; Ge, Yipeng; El Sheikh, Wala; Bawor, Monica; Rangarajan, Sumathy; Dennis, Brittany; Vair, Judith; Sholer, Heather; Hutchinson, Nichole; Iordan, Elizabeth; Mackie, Pam; Samaan, Zainab

2017-09-01

This study aims to assess comparability of a short food frequency questionnaire (SFFQ) used in the Determinants of Suicide: Conventional and Emergent Risk Study (DISCOVER Study) with a validated comprehensive FFQ (CFFQ). A total of 127 individuals completed SFFQ and CFFQ. Healthy eating was measured using Healthy Eating Score (HES). Estimated food intake and healthy eating assessed by SFFQ was compared with the CFFQ. For most food groups and HES, the highest Spearman's rank correlation coefficients between the two FFQs were r > .60. For macro-nutrients, the correlations exceeded 0.4. Cross-classification of quantile analysis showed that participants were classified between 46% and 81% into the exact same quantiles, while 10% or less were misclassified into opposite quantiles. The Bland-Altman plots showed an acceptable level of agreement between the two dietary measurement methods. The SFFQ can be used for Canadian with psychiatric disorders to rank them based on their dietary intake.

Aided diagnosis methods of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Yue; Wang, Nian; Cui, Xiaoyu

2017-08-01

In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.

Can Preoperative Magnetic Resonance Imaging Predict the Reparability of Massive Rotator Cuff Tears?

PubMed

Kim, Jung Youn; Park, Ji Seon; Rhee, Yong Girl

2017-06-01

Numerous studies have shown preoperative fatty infiltration of rotator cuff muscles to be strongly negatively correlated with the successful repair of massive rotator cuff tears (RCTs). To assess the association between factors identified on preoperative magnetic resonance imaging (MRI), especially infraspinatus fatty infiltration, and the reparability of massive RCTs. Case-control study; Level of evidence, 3. We analyzed a total of 105 patients with massive RCTs for whom MRI was performed ≤6 months before arthroscopic procedures. The mean age of the patients was 62.7 years (range, 46-83 years), and 46 were men. Among them, complete repair was possible in 50 patients (48%) and not possible in 55 patients (52%). The tangent sign, fatty infiltration of the rotator cuff, and Patte classification were evaluated as predictors of reparability. Using the receiver operating characteristic curve and the area under the curve (AUC), the prediction accuracy of each variable and combinations of variables were measured. Reparability was associated with fatty infiltration of the supraspinatus ( P = .0045) and infraspinatus ( P < .001) muscles, the tangent sign ( P = .0033), and the Patte classification ( P < .001) but not with fatty infiltration of the subscapularis and teres minor ( P = .425 and .132, respectively). The cut-off values for supraspinatus and infraspinatus fatty infiltration were grade >3 and grade >2, respectively. The examination of single variables revealed that infraspinatus fatty infiltration showed the highest AUC value (0.812; sensitivity: 0.86; specificity: 0.76), while the tangent sign showed the lowest AUC value (0.626; sensitivity: 0.38; specificity: 0.87). Among 2-variable combinations, the combination of infraspinatus fatty infiltration and the Patte classification showed the highest AUC value (0.874; sensitivity: 0.54; specificity: 0.96). The combination of 4 variables, that is, infraspinatus and supraspinatus fatty infiltration, the tangent sign, and the Patte classification, had an AUC of 0.866 (sensitivity: 0.28; specificity: 0.98), which was lower than the highest AUC value (0.874; sensitivity: 0.54; specificity: 0.96) among the 2-variable combinations. The tangent sign or Patte classification alone was not a predictive indicator of the reparability of massive RCTs. Among single variables, infraspinatus fatty infiltration was the most effective in predicting reparability, while the combination of Goutallier classification <3 of the infraspinatus and Patte classification ≤2 of the rotator cuff muscles was the most predictive among the combinations of variables. This information may help predict the reparability of massive RCTs.

77 FR 66715 - Fluridone; Pesticide Tolerances for Emergency Exemptions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-07

..., or pesticide manufacturer. The following list of North American Industrial Classification System... for which there is reliable information.'' This includes exposure through drinking water and in... emergency conditions, EPA has not made any decisions about whether fluridone meets FIFRA's registration...

Lyme Disease: Emergency Department Considerations.

PubMed

Applegren, Nathan D; Kraus, Chadd K

2017-06-01

Lyme disease (LD) is the most common vector-borne illness in North America. Reported cases of LD have increased from approximately 10,000 cases annually in 1991 to >25,000 cases in 2014. Greater recognition, enhanced surveillance, and public education have contributed to the increased prevalence, as have geographic expansion and the number of infected ticks. Cases are reported primarily in the Northeastern United States, Wisconsin, and Minnesota, with children having the highest incidence of LD among all age groups. The increased incidence and prevalence of LD in the United States makes it increasingly more common for patients to present to the emergency department (ED) for tick bites and LD-related chief complaints, such as the characteristic erythema migrans skin manifestation. We sought to review the etiology of LD, describe its clinical presentations and sequela, and provide a practical classification and approach to ED management of patients with LD-related presentations. In this review, ED considerations for LD are presented and clinical presentations and management of the disease at different stages is discussed. Delayed sequelae that have significant morbidity, including Lyme carditis and Lyme neuroborreliosis, are discussed. Diagnostic tests and management are described in detail. The increasing prevalence and growing geographic reach of Lyme disease makes it critically important for emergency physicians to consider the diagnosis in patients presenting with symptoms suggestive of LD and to initiate appropriate treatment to minimize the potential of delayed sequelae. Special consideration should be made for the epidemiology of LD and a high clinical suspicion should be present for patients in endemic areas or with known exposures to ticks. Emergency physicians can play a critical role in the recognition, diagnosis, and treatment of LD. Copyright © 2017 Elsevier Inc. All rights reserved.

A Comparison of Jefferson Medical College Graduates Who Chose Emergency Medicine with Those Who Chose Other Specialties.

ERIC Educational Resources Information Center

Xu, Gang; Veloski, J. Jon

1991-01-01

Data on 53 Jefferson Medical College (Pennsylvania) graduates specializing in emergency medicine (EM) found they had the highest senior year debt and expected the highest income among nonsurgeons, compared favorably in academic performance and examination scores and were very willing to treat low-income patients. Implications are discussed.…

The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

PubMed

Banan, Rouzbeh; Hartmann, Christian

2017-03-01

The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only established in a small number of institutions. In summary, while neuropathologists have now encountered various challenges in the transitional phase from the previous WHO 2007 version to the new WHO 2016 classification of brain tumors, clinical neurooncologists now face many new diagnoses allowing a clearly improved understanding that could offer them more effective therapeutic opportunities in neurooncological treatment. The new WHO 2016 classification presumably presents the highest number of modifications since the initial WHO classification of 1979 and thereby forces all professionals in the field of neurooncology to intensively understand the new concepts. This review article aims to present the basic concepts of the new WHO 2016 brain tumor classification for neurosurgeons with a focus on neurooncology.

Information as Commodity and Economic Sector: Its Emergence in the Discourse of Industrial Classification.

ERIC Educational Resources Information Center

Malone, Cheryl Knott; Elichirigoity, Fernando

2003-01-01

Provides a critical analysis of the development and deployment of the North American Industry Classification System (NAICS), focusing on discourse surrounding creation of the system's "information" category. Suggests that it functions to position information as a major sector of the economy and to organize data about information as a…

Classification of "multipole" superconductivity in multiorbital systems and its implications

NASA Astrophysics Data System (ADS)

Nomoto, T.; Hattori, K.; Ikeda, H.

2016-11-01

Motivated by a growing interest in multiorbital superconductors with spin-orbit interactions, we perform the group-theoretical classification of various unconventional superconductivity emerging in symmorphic O , D4, and D6 space groups. The generalized Cooper pairs, which we here call "multipole" superconductivity, possess spin-orbital coupled (multipole) degrees of freedom, instead of the conventional spin singlet/triplet in single-orbital systems. From the classification, we obtain the following key consequences, which have never been focused in the long history of research in this field: (1) A superconducting gap function with Γ9⊗Γ9 in D6 possesses nontrivial momentum dependence different from the usual spin-1/2 classification. (2) Unconventional gap structure can be realized in the BCS approximation of purely local (onsite) interactions irrespective of attraction/repulsion. It implies the emergence of an electron-phonon (e-ph) driven unconventional superconductivity. (3) Reflecting symmetry of orbital basis functions there appear not symmetry protected but inevitable line nodes/gap minima, and thus, anisotropic s -wave superconductivity can be naturally explained even in the absence of competing fluctuations.

High blood Pressure in children and its correlation with three definitions of obesity in childhood

PubMed Central

de Moraes, Leonardo Iezzi; Nicola, Thaís Coutinho; de Jesus, Julyanna Silva Araújo; Alves, Eduardo Roberty Badiani; Giovaninni, Nayara Paula Bernurdes; Marcato, Daniele Gasparini; Sampaio, Jéssica Dutra; Fuly, Jeanne Teixeira Bessa; Costalonga, Everlayny Fiorot

2014-01-01

Background Several authors have correlated the increase of cardiovascular risk with the nutritional status, however there are different criteria for the classification of overweight and obesity in children. Objectives To evaluate the performance of three nutritional classification criteria in children, as definers of the presence of obesity and predictors of high blood pressure in schoolchildren. Methods Eight hundred and seventeen children ranging 6 to 13 years old, enrolled in public schools in the municipality of Vila Velha (ES) were submitted to anthropometric evaluation and blood pressure measurement. The classification of the nutritional status was established by two international criteria (CDC/NCHS 2000 and IOTF 2000) and one Brazilian criterion (Conde e Monteiro 2006). Results The prevalence of overweight was higher when the criterion of Conde e Monteiro (27%) was used, and inferior by the IOTF (15%) criteria. High blood pressure was observed in 7.3% of children. It was identified a strong association between the presence of overweight and the occurrence of high blood pressure, regardless of the test used (p < 0.001). The test showing the highest sensitivity in predicting elevated BP was the Conde e Monteiro (44%), while the highest specificity (94%) and greater overall accuracy (63%), was the CDC criterion. Conclusions The prevalence of overweight in Brazilian children is higher when using the classification criterion of Conde e Monteiro, and lower when the criterion used is IOTF. The Brazilian classification criterion proved to be the most sensitive predictor of high BP risk in this sample. PMID:24676372

Crop identification from radar imagery of the Huntington County, Indiana test site

NASA Technical Reports Server (NTRS)

Batlivala, P. P.; Ulaby, F. T. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Like polarization was successful in discriminating corn and soybeans; however, pasture and woods were consistently confused as soybeans and corn, respectively. The probability of correct classification was about 65%. The cross polarization component (highest for woods and lowest for pasture) helped in separating the woods from corn, and pasture from soybeans, and when used with the like polarization component, the probability of correct classification increased to 74%.

Unrealistic phylogenetic trees may improve phylogenetic footprinting.

PubMed

Nettling, Martin; Treutler, Hendrik; Cerquides, Jesus; Grosse, Ivo

2017-06-01

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily. Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting. The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo. : martin.nettling@informatik.uni-halle.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

NASA Astrophysics Data System (ADS)

Marwaha, Richa; Kumar, Anil; Kumar, Arumugam Senthil

2015-01-01

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

Classification deficits in Alzheimer's disease with special reference to living and nonliving things.

PubMed

Montanes, P; Goldblum, M C; Boller, F

1996-08-01

The present study was conducted to assess the hypothesis that visual similarity between exemplars within a semantic category may affect differentially the recognition process of living and nonliving things, according to task demands, in patients with semantic memory disorders. Thirty-nine Alzheimer's patients and 39 normal elderly subjects were presented with a task in which they had to classify pictures and words, depicting either living or nonliving things, at two levels of classification: subordinate (e.g., mammals versus birds or tools versus vehicles) and attribute (e.g., wild versus domestic animals or fast versus slow vehicles). Contrary to previous results (Montañes, Goldblum, & Boller, 1995) in a naming task, but as expected, living things were better classified than nonliving ones by both controls and patients. As expected, classifications at the subordinate level also gave rise to better performance than classifications at the attribute level. Although (and somewhat unexpectedly) no advantage of picture over word classification emerged, some effects consistent with the hypothesis that visual similarity affects picture classification emerged, in particular within a subgroup of patients with predominant verbal deficits and the most severe semantic memory disorders. This subgroup obtained a better score on classification of pictures than of words depicting living items (that share many visual features) when classification is at the subordinate level (for which visual similarity is a reliable clue to classification), but met with major difficulties when classifying those pictures at the attribute level (for which shared visual features are not reliable clues to classification). These results emphasize the fact that some "normal" effects specific to items in living and nonliving categories have to be considered among the factors causing selective category-specific deficits in patients, as well as their relevance in achieving tasks which require either differentiation between competing exemplars in the same semantic category (naming) or detection of resemblance between those exemplars (categorization).

Molecular diagnostics in the management of rhabdomyosarcoma.

PubMed

Arnold, Michael A; Barr, Fredric G

2017-02-01

A classification of rhabdomyosarcoma (RMS) with prognostic relevance has primarily relied on clinical features and histologic classification as either embryonal or alveolar RMS. The PAX3-FOXO1 and PAX7-FOXO1 gene fusions occur in 80% of cases with the alveolar subtype and are more predictive of outcome than histologic classification. Identifying additional molecular hallmarks that further subclassify RMS is an active area of research. Areas Covered: The authors review the current state of the PAX3-FOXO1 and PAX7-FOXO1 fusions as prognostic biomarkers. Emerging biomarkers, including mRNA expression profiling, MYOD1 mutations, RAS pathway mutations and gene fusions involving NCOA2 or VGLL2 are also reviewed. Expert commentary: Strategies for modifying RMS risk stratification based on molecular biomarkers are emerging with the potential to transform the clinical management of RMS, ultimately improving patient outcomes by tailoring therapy to predicted patient risk and identifying targets for novel therapies.

76 FR 55799 - Mandipropamid; Pesticide Tolerances for Emergency Exemptions

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-09

... affected. The North American Industrial Classification System (NAICS) codes have been provided to assist....'' This includes exposure through drinking water and in residential settings, but does not include... are being approved under emergency conditions, EPA has not made any decisions about whether...

A full computation-relevant topological dynamics classification of elementary cellular automata.

PubMed

Schüle, Martin; Stoop, Ruedi

2012-12-01

Cellular automata are both computational and dynamical systems. We give a complete classification of the dynamic behaviour of elementary cellular automata (ECA) in terms of fundamental dynamic system notions such as sensitivity and chaoticity. The "complex" ECA emerge to be sensitive, but not chaotic and not eventually weakly periodic. Based on this classification, we conjecture that elementary cellular automata capable of carrying out complex computations, such as needed for Turing-universality, are at the "edge of chaos."

The history of transdisciplinary race classification: methods, politics and institutions, 1840s-1940s.

PubMed

McMahon, Richard

2018-03-01

A recently blossoming historiographical literature recognizes that physical anthropologists allied with scholars of diverse aspects of society and history to racially classify European peoples over a period of about a hundred years. They created three successive race classification coalitions - ethnology, from around 1840; anthropology, from the 1850s; and interwar raciology - each of which successively disintegrated. The present genealogical study argues that representing these coalitions as 'transdisciplinary' can enrich our understanding of challenges to disciplinary specialization. This is especially the case for the less well-studied nineteenth century, when disciplines and challenges to disciplinary specialization were both gradually emerging. Like Marxism or structuralism, race classification was a holistic interpretive framework, which, at its most ambitious, aimed to structure the human sciences as a whole. It resisted the organization of academia and knowledge into disciplines with separate organizational institutions and research practices. However, the 'transdisciplinarity' of this nationalistic project also bridged emerging borderlines between science and politics. I ascribe race classification's simultaneous longevity and instability to its complex and intricately entwined processes of political and interdisciplinary coalition building. Race classification's politically useful conclusions helped secure public support for institutionalizing the coalition's component disciplines. Institutionalization in turn stimulated disciplines to professionalize. They emphasized disciplinary boundaries and insisted on apolitical science, thus ultimately undermining the 'transdisciplinary' project.

Classification of urban features using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Ganesh Babu, Bharath

Accurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods -- maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the potential for using the visible and near infrared (VNIR) bands from AISA+ hyperspectral data in urban LULC classification. Based on their performance, the need for further research using ECHO and SAM is underscored. The importance incorporating imaging spectrometer data in high resolution urban feature mapping is emphasized.

Emerging Technologies Program Integration Report. Volume 2. Background, Delphi and Workshop Data. Appendices

DTIC Science & Technology

1987-05-04

FTIILE COP’ AD-A196 840 EMERGING TECHNOLOGIES PROGRAM INTEGRATION REPORT VOLUME II BACKGROUND, DELPHI AND WORKSHOP DATA, APPENDICES . -- PREPARED...Security Classification) Emerging Technologies Program Integration Report Volume II: Background, Delphi and Workshop Data; Appendices (U) 12 PERSONAL...volumes of this integration report assess and synthesize information gathered through a Delphi survey, defense needs prioritization workshops, and

32 CFR Appendix C to Part 290 - For Official Use Only

Code of Federal Regulations, 2013 CFR

2013-07-01

... the top and bottom with the highest security classification of information appearing on the page. (iii... the additional expense balanced against the degree of sensitivity of the type of FOUO information...

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance.

PubMed

Liljeqvist, Henning T G; Muscatello, David; Sara, Grant; Dinh, Michael; Lawrence, Glenda L

2014-09-23

Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits. We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other's classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A "mental health problem presentation" for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians' assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic. Agreement between clinician's classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 - 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99). Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

Field validation of Burned Area Reflectance Classification (BARC) products for post fire assessment

Treesearch

Andrew T. Hudak; Peter R. Robichaud; Jeffery B. Evans; Jess Clark; Keith Lannom; Penelope Morgan; Carter Stone

2004-01-01

The USFS Remote Sensing Applications Center (RSAC) and the USGS EROS Data Center (EDC) produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Rehabilitation (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of soil burn severity, but photosynthetic and nonphotosynthetic vegetation also influences the...

Analysis of Particulate Composite Behavior Based on Nonlinear Elasticity and an Improved Mori-Tanaka Theory

DTIC Science & Technology

1998-09-01

to characterize the weakening constraint power of the matrix as opposed to earlier analyses that used an additional eigenstrain term. It also...matrix Poisson ratio was constant and the inclusions were rigid, he showed that the disturbed strain and the eigenstrain in the Eshelby method could...Eshelby, elastic properties, prediction, energy balance, mechanical behavior, eigenstrain , nonlinear dcd03e So7S&3 UNCLASSIFIED SECURITY CLASSIFICATION OF FORM (Highest classification of Title, Abstract, Keywords)

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications

NASA Astrophysics Data System (ADS)

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications.

PubMed

Makra, László; Puskás, János; Matyasovszky, István; Csépe, Zoltán; Lelovics, Enikő; Bálint, Beatrix; Tusnády, Gábor

2015-09-01

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999-2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

32 CFR Appendix A to Part 295 - For Official Use Only (FOUO)

Code of Federal Regulations, 2013 CFR

2013-07-01

... bottom with the highest security classification of information appearing on the page. (3) Within a... degree of sensitivity of the type of FOUO information contained in the records. (2) Record copies of FOUO...

32 CFR Appendix A to Part 295 - For Official Use Only (FOUO)

Code of Federal Regulations, 2011 CFR

2011-07-01

... bottom with the highest security classification of information appearing on the page. (3) Within a... degree of sensitivity of the type of FOUO information contained in the records. (2) Record copies of FOUO...

Incidence of speech recognition errors in the emergency department.

PubMed

Goss, Foster R; Zhou, Li; Weiner, Scott G

2016-09-01

Physician use of computerized speech recognition (SR) technology has risen in recent years due to its ease of use and efficiency at the point of care. However, error rates between 10 and 23% have been observed, raising concern about the number of errors being entered into the permanent medical record, their impact on quality of care and medical liability that may arise. Our aim was to determine the incidence and types of SR errors introduced by this technology in the emergency department (ED). Level 1 emergency department with 42,000 visits/year in a tertiary academic teaching hospital. A random sample of 100 notes dictated by attending emergency physicians (EPs) using SR software was collected from the ED electronic health record between January and June 2012. Two board-certified EPs annotated the notes and conducted error analysis independently. An existing classification schema was adopted to classify errors into eight errors types. Critical errors deemed to potentially impact patient care were identified. There were 128 errors in total or 1.3 errors per note, and 14.8% (n=19) errors were judged to be critical. 71% of notes contained errors, and 15% contained one or more critical errors. Annunciation errors were the highest at 53.9% (n=69), followed by deletions at 18.0% (n=23) and added words at 11.7% (n=15). Nonsense errors, homonyms and spelling errors were present in 10.9% (n=14), 4.7% (n=6), and 0.8% (n=1) of notes, respectively. There were no suffix or dictionary errors. Inter-annotator agreement was 97.8%. This is the first estimate at classifying speech recognition errors in dictated emergency department notes. Speech recognition errors occur commonly with annunciation errors being the most frequent. Error rates were comparable if not lower than previous studies. 15% of errors were deemed critical, potentially leading to miscommunication that could affect patient care. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Clinical Implications of Cluster Analysis-Based Classification of Acute Decompensated Heart Failure and Correlation with Bedside Hemodynamic Profiles.

PubMed

Ahmad, Tariq; Desai, Nihar; Wilson, Francis; Schulte, Phillip; Dunning, Allison; Jacoby, Daniel; Allen, Larry; Fiuzat, Mona; Rogers, Joseph; Felker, G Michael; O'Connor, Christopher; Patel, Chetan B

2016-01-01

Classification of acute decompensated heart failure (ADHF) is based on subjective criteria that crudely capture disease heterogeneity. Improved phenotyping of the syndrome may help improve therapeutic strategies. To derive cluster analysis-based groupings for patients hospitalized with ADHF, and compare their prognostic performance to hemodynamic classifications derived at the bedside. We performed a cluster analysis on baseline clinical variables and PAC measurements of 172 ADHF patients from the ESCAPE trial. Employing regression techniques, we examined associations between clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry). We assessed association with clinical outcomes using Cox proportional hazards models. Likelihood ratio tests were used to compare the prognostic value of cluster data to that of hemodynamic data. We identified four advanced HF clusters: 1) male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest B-type natriuretic peptide (BNP) levels; 2) females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; 3) young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics, advanced disease; and 4) older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, highest BNP levels. There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70). For all adverse clinical outcomes, Cluster 4 had the highest risk, and Cluster 2, the lowest. Compared to Cluster 4, Clusters 1-3 had 45-70% lower risk of all-cause mortality. Clusters were significantly associated with clinical outcomes, whereas hemodynamic profiles were not. By clustering patients with similar objective variables, we identified four clinically relevant phenotypes of ADHF patients, with no discernable relationship to hemodynamic profiles, but distinct associations with adverse outcomes. Our analysis suggests that ADHF classification using simultaneous considerations of etiology, comorbid conditions, and biomarker levels, may be superior to bedside classifications.

Objective color measurements: clinimetric performance of three devices on normal skin and scar tissue.

PubMed

van der Wal, Martijn; Bloemen, Monica; Verhaegen, Pauline; Tuinebreijer, Wim; de Vet, Henrica; van Zuijlen, Paul; Middelkoop, Esther

2013-01-01

Color measurements are an essential part of scar evaluation. Thus, vascularization (erythema) and pigmentation (melanin) are common outcome parameters in scar research. The aim of this study was to investigate the clinimetric properties and clinical feasibility of the Mexameter, Colorimeter, and the DSM II ColorMeter for objective measurements on skin and scars. Fifty scars with a mean age of 6 years (2 months to 53 years) were included. Reliability was tested using the single-measure interobserver intraclass correlation coefficient. Validity was determined by measuring the Pearson correlation with the Fitzpatrick skin type classification (for skin) and the Patient and Observer Scar Assessment Scale (for scar tissue). All three instruments provided reliable readings (intraclass correlation coefficient ≥ 0.83; confidence interval: 0.71-0.90) on normal skin and scar tissue. Parameters with the highest correlations with the Fitzpatrick classification were melanin (Mexameter), 0.72; ITA (Colorimeter), -0.74; and melanin (DSM II), 0.70. On scars, the highest correlations with the Patient and Observer Scar Assessment Scale vascularization scores were the following: erythema (Mexameter), 0.59; LAB2 (Colorimeter), 0.69; and erythema (DSM II), 0.66. For hyperpigmentation, the highest correlations were melanin (Mexameter), 0.75; ITA (Colorimeter), -0.80; and melanin (DSM II), 0.83. This study shows that all three instruments can provide reliable color data on skin and scars with a single measurement. The authors also demonstrated that they can assist in objective skin type classification. For scar assessment, the most valid parameters in each instrument were identified.

About decomposition approach for solving the classification problem

NASA Astrophysics Data System (ADS)

Andrianova, A. A.

2016-11-01

This article describes the features of the application of an algorithm with using of decomposition methods for solving the binary classification problem of constructing a linear classifier based on Support Vector Machine method. Application of decomposition reduces the volume of calculations, in particular, due to the emerging possibilities to build parallel versions of the algorithm, which is a very important advantage for the solution of problems with big data. The analysis of the results of computational experiments conducted using the decomposition approach. The experiment use known data set for binary classification problem.

The PLATINO study: description of the distribution, stability, and mortality according to the Global Initiative for Chronic Obstructive Lung Disease classification from 2007 to 2017.

PubMed

Menezes, Ana M; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Viana, Karynna P; Soares, Claudia; Müllerova, Hana; Valdivia, Gonzalo; Jardim, José R; Montes de Oca, Maria

2017-01-01

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) report provides a framework for classifying COPD reflecting the impacts of disease on patients and for targeting treatment recommendations. The GOLD 2017 introduced a new classification with 16 subgroups based on a composite of spirometry and symptoms/exacerbations. Data from the population-based PLATINO study, collected at baseline and at follow-up, in three sites in Latin America were analyzed to compare the following: 1) the distribution of COPD patients according to GOLD 2007, 2013, and 2017; 2) the stability of the 2007 and 2013 classifications; and 3) the mortality rate over time stratified by GOLD 2007, 2013, and 2017. Of the 524 COPD patients evaluated, most of them were classified as Grade I or II (GOLD 2007) and Group A or B (GOLD 2013), with ≈70% of those classified as Group A in GOLD 2013 also classified as Grade I in GOLD 2007 and the highest percentage (41%) in Group D (2013) classified as Grade III (2007). According to GOLD 2017, among patients with Grade I airflow limitation, 69% of them were categorized into Group A, whereas Grade IV patients were more evenly distributed among Groups A-D. Most of the patients classified by GOLD 2007 remained in the same airflow limitation group at the follow-up; a greater temporal variability was observed with GOLD 2013 classification. Incidence-mortality rate in patients classified by GOLD 2007 was positively associated with increasing severity of airflow obstruction; for GOLD 2013 and GOLD 2017 (Groups A-D), highest mortality rates were observed in Groups C and D. No clear pattern was observed for mortality across the GOLD 2017 subgroups. The PLATINO study data suggest that GOLD 2007 classification shows more stability over time compared with GOLD 2013. No clear patterns with respect to the distribution of patients or incidence-mortality rates were observed according to GOLD 2013/2017 classification.

A comparison of unsupervised classification procedures on LANDSAT MSS data for an area of complex surface conditions in Basilicata, Southern Italy

NASA Technical Reports Server (NTRS)

Justice, C.; Townshend, J. (Principal Investigator)

1981-01-01

Two unsupervised classification procedures were applied to ratioed and unratioed LANDSAT multispectral scanner data of an area of spatially complex vegetation and terrain. An objective accuracy assessment was undertaken on each classification and comparison was made of the classification accuracies. The two unsupervised procedures use the same clustering algorithm. By on procedure the entire area is clustered and by the other a representative sample of the area is clustered and the resulting statistics are extrapolated to the remaining area using a maximum likelihood classifier. Explanation is given of the major steps in the classification procedures including image preprocessing; classification; interpretation of cluster classes; and accuracy assessment. Of the four classifications undertaken, the monocluster block approach on the unratioed data gave the highest accuracy of 80% for five coarse cover classes. This accuracy was increased to 84% by applying a 3 x 3 contextual filter to the classified image. A detailed description and partial explanation is provided for the major misclassification. The classification of the unratioed data produced higher percentage accuracies than for the ratioed data and the monocluster block approach gave higher accuracies than clustering the entire area. The moncluster block approach was additionally the most economical in terms of computing time.

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

Classifying Radio Galaxies with the Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Aniyan, A. K.; Thorat, K.

2017-06-01

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

[Naïve Bayes classification for classifying injury-cause groups from Emergency Room data in the Friuli Venezia Giulia region (Northern Italy)].

PubMed

Valent, Francesca; Clagnan, Elena; Zanier, Loris

2014-01-01

to assess whether Naïve Bayes Classification could be used to classify injury causes from the Emergency Room (ER) database, because in the Friuli Venezia Giulia Region (Northern Italy) the electronic ER data have never been used to study the epidemiology of injuries, because the proportion of generic "accidental" causes is much higher than that of injuries with a specific cause. application of the Naïve Bayes Classification method to the regional ER database. sensitivity, specificity, positive and negative predictive values, agreement, and the kappa statistic were calculated for the train dataset and the distribution of causes of injury for the test dataset. on 22.248 records with known cause, the classifications assigned by the model agreed moderately (kappa =0.53) with those assigned by ER personnel. The model was then used on 76.660 unclassified cases. Although sensitivity and positive predictive value of the method were generally poor, mainly due to limitations in the ER data, it allowed to estimate for the first time the frequency of specific injury causes in the Region. the model was useful to provide the "big picture" of non-fatal injuries in the Region. To improve the collection of injury data at the ER, the options available for injury classification in the ER software are being revised to make categories exhaustive and mutually exclusive.

Benign paroxysmal positional vertigo: Diagnostic criteria Consensus document of the Committee for the Classification of Vestibular Disorders of the Bárány Society.

PubMed

von Brevern, Michael; Bertholon, Pierre; Brandt, Thomas; Fife, Terry; Imai, Takao; Nuti, Daniele; Newman-Toker, David

This article presents operational diagnostic criteria for benign paroxysmal positional vertigo (BPPV), formulated by the Committee for Classification of Vestibular Disorders of the Bárány Society. The classification reflects current knowledge of clinical aspects and pathomechanisms of BPPV and includes both established and emerging syndromes of BPPV. It is anticipated that growing understanding of the disease will lead to further development of this classification. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.

Categorization in the wild.

PubMed

Glushko, Robert J; Maglio, Paul P; Matlock, Teenie; Barsalou, Lawrence W

2008-04-01

In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important. Furthermore, key aspects of categorization that have received little previous attention emerge from considering diverse types of categorization together, such as the social factors that create stability in classification systems, and the interoperability that shared conceptual systems establish between agents. Finally, the profound impact of recent technological developments on classification systems indicates that basic categorization mechanisms are highly adaptive, producing new classification systems as the situations in which they operate change.

Classification of wetlands and deepwater habitats of the United States

USGS Publications Warehouse

Cowardin, L.M.; Carter, V.; Golet, F.C.; LaRoe, E.T.

1985-01-01

This classification, to be used in a new inventory of wetlands and deepwater habitats of the United States, is intended to describe ecological taxa, arrange them in a system useful to resource managers, furnish units for mapping, and provide uniformity of concepts and terms. Wetlands are defined by plants (hydrophytes), soils (hydric soils), and frequency of flooding. Ecologically related areas of deep water, traditionally not considered wetlands, are included in the classification as deepwater habitats.Systems form the highest level of the classification hierarchy; five are defined-Marine, Estuarine, Riverine, Lacustrine, and Palustrine. Marine and Estuarine Systems each have two Subsystems, Subtidal and Intertidal; the Riverine System has four Subsystems, Tidal, Lower Perennial, Upper Perennial, and Intermittent; the Lacustrine has two, Littoral and Limnetic; and the Palustrine has no Subsystems.Within the Subsystems, Classes are based on substrate material and flooding regime, or on vegetative life form. The same Classes may appear under one or more of the Systems or Subsystems. Six Classes are based on substrate and flooding regime: (1) Rock Bottom with a substrate of bedrock, boulders, or stones; (2) Unconsolidated Bottom with a substrate of cobbles, gravel, sand, mud, or organic material; (3) Rocky Shore with the same substrates as Rock Bottom; (4) Unconsolidated Shore with the same substrates as Unconsolidated Bottom; (5) Streambed with any of the substrates; and (6) Reef with a substrate composed of the living and dead remains of invertebrates (corals, mollusks, or worms). The bottom Classes, (1) and (2) above, are flooded all or most of the time and the shore Classes, (3) and (4), are exposed most of the time. The Class Streambed is restricted to channels of intermittent streams and tidal channels that are dewatered at low tide. The life form of the dominant vegetation defines the five Classes based on vegetative form: (1) Aquatic Bed, dominated by plants that grow principally on or below the surface of the water; (2) Moss-Lichen Wetland, dominated by mosses or lichens; (3) Emergent Wetland, dominated by emergent herbaceous angiosperms; (4) Scrub-Shrub Wetland, dominated by shrubs or small trees; and (5) Forested Wetland, dominated by large trees.The Dominance Type, which is named for the dominant plant or animal forms, is the lowest level of the classification hierarchy. Only examples are provided for this level; Dominance Types must be developed by individual users of the classification.Modifying terms applied to the Classes or Subclasses are essential for use of the system. In tidal areas, the type and duration of flooding are described by four Water Regime Modifiers: subtidal, irregularly exposed, regularly flooded, and irregularly flooded. In nontidal areas, eight Regimes are used: permanently flooded, intermittently exposed, semipermanently flooded, seasonally flooded, saturated, temporarily flooded, intermittently flooded, and artificially flooded. A hierarchical system of Water Chemistry Modifiers, adapted from the Venice System, is used to describe the salinity of the water. Fresh waters are further divided on the basis of pH. Use of a hierarchical system of soil modifiers taken directly from U.S. soil taxonomy is also required. Special modifiers are used where appropriate: excavated, impounded, diked, partly drained, farmed, and artificial.Regional differences important to wetland ecology are described through a regionalization that combines a system developed for inland areas by R. G. Bailey in 1976 with our Marine and Estuarine provinces.The structure of the classification allows it to be used at any of several hierarchical levels. Special data required for detailed application of the system are frequently unavailable, and thus data gathering may be prerequisite to classification. Development of rules by the user will be required for specific map scales. Dominance Types and relationships of plant and anima

Classification of wetlands and deepwater habitats of the United States

USGS Publications Warehouse

Cowardin, L.M.; Carter, V.; Golet, F.C.; LaRoe, E.T.

1979-01-01

This classification, to be used in a new inventory of wetlands and deepwater habitats of the United States, is intended to describe ecological taxa, arrange them in a system useful to resource managers, furnish units for mapping, and provide uniformity of concepts and terms. Wetlands are defined by plants (hydrophytes), soils (hydric soils), and frequency of flooding. Ecologically related areas of deep water, traditionally not considered wetlands, are included in the classification as deepwater habitats.Systems form the highest level of the classification hierarchy; five are defined--Marine, Estuarine, Riverine, Lacustrine, and Palustrine. Marine and Estuarine systems each have two subsystems, Subtidal and Intertidal; the Riverine system has four subsystems, Tidal, Lower Perennial, Upper Perennial, and Intermittent; the Lacustrine has two, Littoral and Limnetic; and the Palustrine has no subsystem.Within the subsystems, classes are based on substrate material and flooding regime, or on vegetative life form. The same classes may appear under one or more of the systems or subsystems. Six classes are based on substrate and flooding regime: (1) Rock Bottom with a substrate of bedrock, boulders, or stones; (2) Unconsolidated Bottom with a substrate of cobbles, gravel, sand, mud, or organic material; (3) Rocky Shore with the same substrate as Rock Bottom; (4) Unconsolidated Shore with the same substrate as Unconsolidated Bottom; (5) Streambed with any of the substrates; and (6) Reef with a substrate composed of the living and dead remains of invertebrates (corals, mollusks, or worms). The bottom classes, (1) and (2) above, are flooded all or most of the time and the shore classes, (3) and (4), are exposed most of the time. The class Streambed is restricted to channels of intermittent streams and tidal channels that are dewatered at low tide. The life form of the dominant vegetation defines the five classes based on vegetative form: (1) Aquatic Bed, dominated by plants that grow principally on or below the surface of the water; (2) Moss-Lichen Wetland, dominated by mosses or lichens; (3) Emergent Wetland, dominated by emergent herbaceous angiosperms; (4) Scrub-Shrub Wetland, dominated by shrubs or small trees; and (5) Forested Wetland, dominated by large trees.The dominance type, which is named for the dominant plant or animal forms, is the lowest level of the classification hierarchy. Only examples are provided for this level; dominance types must be developed by individual users of the classification.Modifying terms applied to the classes or subclasses are essential for use of the system. In tidal areas, the type and duration of flooding are described by four water regime modifiers: subtidal, irregularly exposed, regularly flooded, and irregularly flooded. In nontidal areas, six regimes are used: permanently flooded, intermittently exposed, semipermanently flooded, seasonally flooded, saturated, temporarily flooded, intermittently flooded, and artificially flooded. A hierarchical system of water chemistry modifiers, adapted from the Venice System, is used to describe the salinity of the water. Fresh waters are further divided on the basis of pH. Use of a hierarchical system of soil modifiers taken directly from U.S. soil taxonomy is also required. Special modifiers are used where appropriate: excavated, impounded, diked, partly drained, farmed, and artificial.Regional differences important to wetland ecology are described through a regionalization that combines a system developed for inland areas by R. G. Bailey in 1976 with our Marine and Estuarine provinces.The structure of the classification allows it to be used at any of several hierarchical levels. Special data required for detailed application of the system are frequently unavailable, and thus data gathering may be prerequisite to classification. Development of rules by the user will be required for specific map scales. Dominance types and relationships of plant and animal co

CLARIPED: a new tool for risk classification in pediatric emergencies.

PubMed

Magalhães-Barbosa, Maria Clara de; Prata-Barbosa, Arnaldo; Alves da Cunha, Antonio José Ledo; Lopes, Cláudia de Souza

2016-09-01

To present a new pediatric risk classification tool, CLARIPED, and describe its development steps. Development steps: (i) first round of discussion among experts, first prototype; (ii) pre-test of reliability, 36 hypothetical cases; (iii) second round of discussion to perform adjustments; (iv) team training; (v) pre-test with patients in real time; (vi) third round of discussion to perform new adjustments; (vii) final pre-test of validity (20% of medical treatments in five days). CLARIPED features five urgency categories: Red (Emergency), Orange (very urgent), Yellow (urgent), Green (little urgent) and Blue (not urgent). The first classification step includes the measurement of four vital signs (Vipe score); the second step consists in the urgency discrimination assessment. Each step results in assigning a color, selecting the most urgent one for the final classification. Each color corresponds to a maximum waiting time for medical care and referral to the most appropriate physical area for the patient's clinical condition. The interobserver agreement was substantial (kappa=0.79) and the final pre-test, with 82 medical treatments, showed good correlation between the proportion of patients in each urgency category and the number of used resources (p<0.001). CLARIPED is an objective and easy-to-use tool for simple risk classification, of which pre-tests suggest good reliability and validity. Larger-scale studies on its validity and reliability in different health contexts are ongoing and can contribute to the implementation of a nationwide pediatric risk classification system. Copyright © 2016 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Are distal radius fracture classifications reproducible? Intra and interobserver agreement.

PubMed

Belloti, João Carlos; Tamaoki, Marcel Jun Sugawara; Franciozi, Carlos Eduardo da Silveira; Santos, João Baptista Gomes dos; Balbachevsky, Daniel; Chap Chap, Eduardo; Albertoni, Walter Manna; Faloppa, Flávio

2008-05-01

Various classification systems have been proposed for fractures of the distal radius, but the reliability of these classifications is seldom addressed. For a fracture classification to be useful, it must provide prognostic significance, interobserver reliability and intraobserver reproducibility. The aim here was to evaluate the intraobserver and interobserver agreement of distal radius fracture classifications. This was a validation study on interobserver and intraobserver reliability. It was developed in the Department of Orthopedics and Traumatology, Universidade Federal de São Paulo - Escola Paulista de Medicina. X-rays from 98 cases of displaced distal radius fracture were evaluated by five observers: one third-year orthopedic resident (R3), one sixth-year undergraduate medical student (UG6), one radiologist physician (XRP), one orthopedic trauma specialist (OT) and one orthopedic hand surgery specialist (OHS). The radiographs were classified on three different occasions (times T1, T2 and T3) using the Universal (Cooney), Arbeitsgemeinschaft für Osteosynthesefragen/Association for the Study of Internal Fixation (AO/ASIF), Frykman and Fernández classifications. The kappa coefficient (kappa) was applied to assess the degree of agreement. Among the three occasions, the highest mean intraobserver k was observed in the Universal classification (0.61), followed by Fernández (0.59), Frykman (0.55) and AO/ASIF (0.49). The interobserver agreement was unsatisfactory in all classifications. The Fernández classification showed the best agreement (0.44) and the worst was the Frykman classification (0.26). The low agreement levels observed in this study suggest that there is still no classification method with high reproducibility.

Command History, 1971. Supplement. Sanitized

DTIC Science & Technology

1971-01-01

NATIONALS. The security classification for each page is that of the highest classified paragraph thereon. Reproduction of TOP SECRET portions of the 1971...BROWN STALLION (Laos). The primary objective was to enlist assistance of the local populace. -- The Thong Cam magazine, a PSYOP publication, was

Development of pediatric emergency medicine at Addis Ababa University/Tikuranbessa Specialized Hospital, Ethiopia.

PubMed

Tefera, Muluwork; Bacha, Tigist; Butteris, Sabrina; Teshome, Getachew; Ross, Joshua; Hagen, Scott; Svenson, Jim; Busse, Heidi; Tefera, Girma

2014-07-01

In the world emergencies occur everywhere, and each day they consume ressources regardless of whether there are systems capable of achieving good outcomes. Low-income countries suffer the most highest rates of every category of injury--from traffic and the highest rates of acute complications of communicable diseases including tuberculosis, malaria and HIV. To describe the development of pediatrics emergency medicine at Tikur Anbesa Specialized Hospital A twinning partnership model was used in developing a pediatric emergency medicine training program helps in development of pediatrics emergency system. Strengthening the capacity of Addis Ababa University (AAU), Tikur Anbessa Hospital (TASH) to provide pediatric emergency medical services through improved organization of the pediatrics emergency department and strengthening of continuing education opportunities for faculty and staff capacity building by this improving quality of care in pediatrics patients in the country. The Addis Ababa University, University of Wiscosin and People to People partners intend to continue working together to strengthening and developing effetive systems to deliver quality pediatrics emergency medicine care troughout all regions of Ethiopia.

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

PubMed

Hasheminejad, Seyed Hessam-Allah; Zahedi, Mohsen; Hasheminejad, Seyed Mohammad Hossein

2018-03-01

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.

Seismic risk assessment of Navarre (Northern Spain)

NASA Astrophysics Data System (ADS)

Gaspar-Escribano, J. M.; Rivas-Medina, A.; García Rodríguez, M. J.; Benito, B.; Tsige, M.; Martínez-Díaz, J. J.; Murphy, P.

2009-04-01

The RISNA project, financed by the Emergency Agency of Navarre (Northern Spain), aims at assessing the seismic risk of the entire region. The final goal of the project is the definition of emergency plans for future earthquakes. With this purpose, four main topics are covered: seismic hazard characterization, geotechnical classification, vulnerability assessment and damage estimation to structures and exposed population. A geographic information system is used to integrate, analyze and represent all information colleted in the different phases of the study. Expected ground motions on rock conditions with a 90% probability of non-exceedance in an exposure time of 50 years are determined following a Probabilistic Seismic Hazard Assessment (PSHA) methodology that includes a logic tree with different ground motion and source zoning models. As the region under study is located in the boundary between Spain and France, an effort is required to collect and homogenise seismological data from different national and regional agencies. A new homogenised seismic catalogue, merging data from Spanish, French, Catalonian and international agencies and establishing correlations between different magnitude scales, is developed. In addition, a new seismic zoning model focused on the study area is proposed. Results show that the highest ground motions on rock conditions are expected in the northeastern part of the region, decreasing southwards. Seismic hazard can be expressed as low-to-moderate. A geotechnical classification of the entire region is developed based on surface geology, available borehole data and morphotectonic constraints. Frequency-dependent amplification factors, consistent with code values, are proposed. The northern and southern parts of the region are characterized by stiff and soft soils respectively, being the softest soils located along river valleys. Seismic hazard maps including soil effects are obtained by applying these factors to the seismic hazard maps on rock conditions (for the same probability level). Again, the highest hazard is found in the northeastern part of the region. The lowest hazard is obtained along major river valleys The vulnerability assessment of the Navarra building stock is accomplished using as proxy a combination of building age, location, number of floors and the implantation of building codes. Field surveys help constraining the extent of traditional and technological construction types. The vulnerability characterization is carried out following three methods: European Macroseismic Scale (EMS 98), RISK UE vulnerability index and the capacity spectrum method implemented in Hazus. Vulnerability distribution maps for each Navarrean municipality are provided, adapted to the EMS98 vulnerability classes. The vulnerability of Navarre is medium to high, except for recent urban, highly populated developments. For each vulnerability class and expected ground motion, damage distribution is estimated by means of damage probability matrixes. Several damage indexes, embracing relative and absolute damage estimates, are used. Expected average damage is low. Whereas the largest amounts of damaged structures are found in big cities, the highest percentages are obtained in some muniucipalities of northeastern Navarre. Additionally, expected percentages and amounts of affected persons by earthquake damage are calculated for each municipality. Expected amounts of affected people are low, reflecting the low expected damage degree.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover

NASA Astrophysics Data System (ADS)

Li, Mengmeng; Bijker, Wietske; Stein, Alfred

2015-04-01

Two main challenges are faced when classifying urban land cover from very high resolution satellite images: obtaining an optimal image segmentation and distinguishing buildings from other man-made objects. For optimal segmentation, this work proposes a hierarchical representation of an image by means of a Binary Partition Tree (BPT) and an unsupervised evaluation of image segmentations by energy minimization. For building extraction, we apply fuzzy sets to create a fuzzy landscape of shadows which in turn involves a two-step procedure. The first step is a preliminarily image classification at a fine segmentation level to generate vegetation and shadow information. The second step models the directional relationship between building and shadow objects to extract building information at the optimal segmentation level. We conducted the experiments on two datasets of Pléiades images from Wuhan City, China. To demonstrate its performance, the proposed classification is compared at the optimal segmentation level with Maximum Likelihood Classification and Support Vector Machine classification. The results show that the proposed classification produced the highest overall accuracies and kappa coefficients, and the smallest over-classification and under-classification geometric errors. We conclude first that integrating BPT with energy minimization offers an effective means for image segmentation. Second, we conclude that the directional relationship between building and shadow objects represented by a fuzzy landscape is important for building extraction.

Classifying Radio Galaxies with the Convolutional Neural Network

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

Aniyan, A. K.; Thorat, K.

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categoriesmore » is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.« less

[Quantitative classification in catering trade and countermeasures of supervision and management in Hunan Province].

PubMed

Liu, Xiulan; Chen, Lizhang; He, Xiang

2012-02-01

To analyze the status quo of quantitative classification in Hunan Province catering industry, and to discuss the countermeasures in-depth. According to relevant laws and regulations, and after referring to Daily supervision and quantitative scoring sheet and consulting experts, a checklist of key supervision indicators was made. The implementation of quantitative classification in 10 cities in Hunan Province was studied, and the status quo was analyzed. All the 390 catering units implemented quantitative classified management. The larger the catering enterprise, the higher level of quantitative classification. In addition to cafeterias, the smaller the catering units, the higher point of deduction, and snack bars and beverage stores were the highest. For those quantified and classified as C and D, the point of deduction was higher in the procurement and storage of raw materials, operation processing and other aspects. The quantitative classification of Hunan Province has relatively wide coverage. There are hidden risks in food security in small catering units, snack bars, and beverage stores. The food hygienic condition of Hunan Province needs to be improved.

Debugging Techniques Used by Experienced Programmers to Debug Their Own Code.

DTIC Science & Technology

1990-09-01

IS. NUMBER OF PAGES code debugging 62 computer programmers 16. PRICE CODE debug programming 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 119...Davis, and Schultz (1987) also compared experts and novices, but focused on the way a computer program is represented cognitively and how that...of theories in the emerging computer programming domain (Fisher, 1987). In protocol analysis, subjects are asked to talk/think aloud as they solve

New tumor entities in the 4th edition of the World Health Organization classification of head and neck tumors: Nasal cavity, paranasal sinuses and skull base.

PubMed

Thompson, Lester D R; Franchi, Alessandro

2018-03-01

The World Health Organization recently published the 4th edition of the Classification of Head and Neck Tumors, including several new entities, emerging entities, and significant updates to the classification and characterization of tumor and tumor-like lesions, specifically as it relates to nasal cavity, paranasal sinuses, and skull base in this overview. Of note, three new entities (NUT carcinoma, seromucinous hamartoma, biphenotypic sinonasal sarcoma,) were added to this section, while emerging entities (SMARCB1-deficient carcinoma and HPV-related carcinoma with adenoid cystic-like features) and several tumor-like entities (respiratory epithelial adenomatoid hamartoma, chondromesenchymal hamartoma) were included as provisional diagnoses or discussed in the setting of the differential diagnosis. The sinonasal tract houses a significant diversity of entities, but interestingly, the total number of entities has been significantly reduced by excluding tumor types if they did not occur exclusively or predominantly at this site or if they are discussed in detail elsewhere in the book. Refinements to nomenclature and criteria were provided to sinonasal papilloma, borderline soft tissue tumors, and neuroendocrine neoplasms. Overall, the new WHO classification reflects the state of current understanding for many relatively rare neoplasms, with this article highlighting the most significant changes.

Emerging insights into the molecular and cellular basis of glioblastoma

PubMed Central

Dunn, Gavin P.; Rinne, Mikael L.; Wykosky, Jill; Genovese, Giannicola; Quayle, Steven N.; Dunn, Ian F.; Agarwalla, Pankaj K.; Chheda, Milan G.; Campos, Benito; Wang, Alan; Brennan, Cameron; Ligon, Keith L.; Furnari, Frank; Cavenee, Webster K.; Depinho, Ronald A.; Chin, Lynda; Hahn, William C.

2012-01-01

Glioblastoma is both the most common and lethal primary malignant brain tumor. Extensive multiplatform genomic characterization has provided a higher-resolution picture of the molecular alterations underlying this disease. These studies provide the emerging view that “glioblastoma” represents several histologically similar yet molecularly heterogeneous diseases, which influences taxonomic classification systems, prognosis, and therapeutic decisions. PMID:22508724

Women as University Presidents: Navigating the Administrative Labyrinth

ERIC Educational Resources Information Center

Reis, Tania Carlson; Grady, Marilyn L.

2018-01-01

Eleven of the 81 public research universities within the Carnegie Classification of Doctoral Universities: Highest Research are led by woman presidents. Using Eagly & Carli's (2007) labyrinth framework, five of the women presidents were interviewed to identify their experiences navigating leadership barriers. Findings indicated that women…

32 CFR 2001.46 - Transmission.

Code of Federal Regulations, 2013 CFR

2013-07-01

... sender and the intended recipient, the highest classification level of the contents, and any appropriate... transmitted in this manner. (3) Confidential. Confidential information shall be transmitted by any of the... recipient is a U.S. Government facility, the Confidential information may be transmitted via U.S. First...

Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging.

PubMed

Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Atul

2017-12-01

There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Landscape of long non-coding RNA classification

PubMed Central

St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

2015-01-01

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long non-coding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the non-coding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual un-ambiguous classification framework results in a number of challenges in the annotation and interpretation of non-coding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. PMID:25869999

Medical and Endoscopic Management of Gastric Varices

PubMed Central

Al-Osaimi, Abdullah M. S.; Caldwell, Stephen H.

2011-01-01

In the past 20 years, our understanding of the pathophysiology and management options among patients with gastric varices (GV) has changed significantly. GV are the most common cause of upper gastrointestinal bleeding in patients with portal hypertension after esophageal varices (EV) and generally have more severe bleeding than EV. In the United States, the majority of GV patients have underlying portal hypertension rather than splenic vein thrombosis. The widely used classifications are the Sarin Endoscopic Classification and the Japanese Vascular Classifications. The former is based on the endoscopic appearance and location of the varices, while the Japanese classification is based on the underlying vascular anatomy. In this article, the authors address the current concepts of classification, epidemiology, pathophysiology, and emerging management options of gastric varices. They describe the stepwise approach to patients with gastric varices, including the different available modalities, and the pearls, pitfalls, and stop-gap measures useful in managing patients with gastric variceal bleed. PMID:22942544

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

NASA Astrophysics Data System (ADS)

Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

2013-05-01

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

Variable classifications of glycemic index determined by glucose meters.

PubMed

Lin, Meng-Hsueh Amanda; Wu, Ming-Chang; Lin, Jenshinn

2010-07-01

THE STUDY EVALUATED AND COMPARED THE DIFFERENCES OF GLUCOSE RESPONSES, INCREMENTAL AREA UNDER CURVE (IAUC), GLYCEMIC INDEX (GI) AND THE CLASSIFICATION OF GI VALUES BETWEEN MEASURED BY BIOCHEMICAL ANALYZER (FUJI AUTOMATIC BIOCHEMISTRY ANALYZER (FAA)) AND THREE GLUCOSE METERS: Accue Chek Advantage (AGM), BREEZE 2 (BGM), and Optimum Xceed (OGM). Ten healthy subjects were recruited for the study. The results showed OGM yield highest postprandial glucose responses of 119.6 +/- 1.5, followed by FAA, 118.4 +/- 1.2, BGM, 117.4 +/- 1.4 and AGM, 112.6 +/- 1.3 mg/dl respectively. FAA reached highest mean IAUC of 4156 +/- 208 mg x min/dl, followed by OGM (3835 +/- 270 mg x min/dl), BGM (3730 +/- 241 mg x min/dl) and AGM (3394 +/- 253 mg x min/dl). Among four methods, OGM produced highest mean GI value than FAA (87 +/- 5) than FAA, followed by BGM and AGM (77 +/- 1, 68 +/- 4 and 63 +/- 5, p<0.05). The results suggested that the AGM, BGM and OGM are more variable methods to determine IAUC, GI and rank GI value of food than FAA. The present result does not necessarily apply to other glucose meters. The performance of glucose meter to determine GI value of food should be evaluated and calibrated before use.

[Contribution of the Duke's classification in the emergency department in the early management of infective endocarditis].

PubMed

Hautain, C; Delleuze, P; Godefroid, C; Vranckx, M

2015-01-01

The diagnosis of infective endocarditis is based on multiple clinical signs than on a single positive test result. The contribution of echocardiography is an indispensable asset to avoid misdiagnosis or delayed correct diagnosis. A 24-year old woman is admitted to the emergency room. She has a poor general condition, pyrexia and necrotic lesions on the body. After examination, the diagnosis of multiple organ failure and severe sepsis from infective endocarditis from intravenous injections of cocaine is made and the patient is transferred to ICU. She is treated with vancomycin for 4 weeks and gentamicin for 8 days. Her clinical improvement allows her to be transferred to a hospital unit at day 6. She goes home after 28 days of hospitalization. Several sets of criteria for the diagnosis of infective endocarditis are described. The most commonly accepted are revised Duke's criteria that take into account echocardiography. This article aims, through a clinical case, to describe this classification too little used in the emergency room.

A decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: a cohort study.

PubMed

Esteban, Cristóbal; Arostegui, Inmaculada; Garcia-Gutierrez, Susana; Gonzalez, Nerea; Lafuente, Iratxe; Bare, Marisa; Fernandez de Larrea, Nerea; Rivas, Francisco; Quintana, José M

2015-12-22

Creating an easy-to-use instrument to identify predictors of short-term (30/60-day) mortality after an exacerbation of chronic obstructive pulmonary disease (eCOPD) could help clinicians choose specific measures of medical care to decrease mortality in these patients. The objective of this study was to develop and validate a classification and regression tree (CART) to predict short term mortality among patients evaluated in an emergency department (ED) for an eCOPD. We conducted a prospective cohort study including participants from 16 hospitals in Spain. COPD patients with an exacerbation attending the emergency department (ED) of any of the hospitals between June 2008 and September 2010 were recruited. Patients were randomly divided into derivation (50%) and validation samples (50%). A CART based on a recursive partitioning algorithm was created in the derivation sample and applied to the validation sample. Two thousand four hundred eighty-seven patients, 1252 patients in the derivation sample and 1235 in the validation sample, were enrolled in the study. Based on the results of the univariate analysis, five variables (baseline dyspnea, cardiac disease, the presence of paradoxical breathing or use of accessory inspiratory muscles, age, and Glasgow Coma Scale score) were used to build the CART. Mortality rates 30 days after discharge ranged from 0% to 55% in the five CART classes. The lowest mortality rate was for the branch composed of low baseline dyspnea and lack of cardiac disease. The highest mortality rate was in the branch with the highest baseline dyspnea level, use of accessory inspiratory muscles or paradoxical breathing upon ED arrival, and Glasgow score <15. The area under the receiver-operating curve (AUC) in the derivation sample was 0.835 (95% CI: 0.783, 0.888) and 0.794 (95% CI: 0.723, 0.865) in the validation sample. CART was improved to predict 60-days mortality risk by adding the Charlson Comorbidity Index, reaching an AUC in the derivation sample of 0.817 (95% CI: 0.776, 0.859) and 0.770 (95% CI: 0.716, 0.823) in the validation sample. We identified several easy-to-determine variables that allow clinicians to classify eCOPD patients by short term mortality risk, which can provide useful information for establishing appropriate clinical care. NCT02434536.

A forward-looking, national-scale remote sensing-based model of tidal marsh aboveground carbon stocks

NASA Astrophysics Data System (ADS)

Holmquist, J. R.; Byrd, K. B.; Ballanti, L.; Nguyen, D.; Simard, M.; Windham-Myers, L.; Thomas, N.

2017-12-01

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our goal was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest algorithm we tested Sentinel-1 radar backscatter metrics and Landsat vegetation indices as predictors of biomass. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=310 g/m2, 10.3% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. Louisiana palustrine emergent marshes had the highest C density (2.67 ±0.08 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ±0.06 Mg/ha). This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included for the first time in the 2018 U.S. EPA Greenhouse Gas Inventory for coastal wetlands. As technical barriers have been reduced through the availability of free post-processed satellite data, cloud computing platforms and open source software, this approach can potentially be applied globally as well.

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

PubMed

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

PubMed

Zhang, He-Hua; Yang, Liuyang; Liu, Yuchuan; Wang, Pin; Yin, Jun; Li, Yongming; Qiu, Mingguo; Zhu, Xueru; Yan, Fang

2016-11-16

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

46 CFR 169.309 - Structural standards.

Code of Federal Regulations, 2011 CFR

2011-10-01

... adequate strength to withstand the highest loadings imposed by the sail systems during all normal and... calculations with respect to the strength of the sail system may be required. Approval by a recognized classification society may be considered satisfactory evidence of the adequacy of the sail system. (d) When...

46 CFR 169.309 - Structural standards.

Code of Federal Regulations, 2010 CFR

2010-10-01

... adequate strength to withstand the highest loadings imposed by the sail systems during all normal and... calculations with respect to the strength of the sail system may be required. Approval by a recognized classification society may be considered satisfactory evidence of the adequacy of the sail system. (d) When...

Towards an Automated Classification of Transient Events in Synoptic Sky Surveys

NASA Technical Reports Server (NTRS)

Djorgovski, S. G.; Donalek, C.; Mahabal, A. A.; Moghaddam, B.; Turmon, M.; Graham, M. J.; Drake, A. J.; Sharma, N.; Chen, Y.

2011-01-01

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel featrue is the development of an automated follow-up recommendation engine, that suggest those measruements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost funcations. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging disciplne of AstroInformatics.

The dynamics of human-induced land cover change in miombo ecosystems of southern Africa

NASA Astrophysics Data System (ADS)

Jaiteh, Malanding Sambou

Understanding human-induced land cover change in the miombo require the consistent, geographically-referenced, data on temporal land cover characteristics as well as biophysical and socioeconomic drivers of land use, the major cause of land cover change. The overall goal of this research to examine the applications of high-resolution satellite remote sensing data in studying the dynamics of human-induced land cover change in the miombo. Specific objectives are to: (1) evaluate the applications of computer-assisted classification of Landsat Thematic Mapper (TM) data for land cover mapping in the miombo and (2) analyze spatial and temporal patterns of landscape change locations in the miombo. Stepwise Thematic Classification, STC (a hybrid supervised-unsupervised classification) procedure for classifying Landsat TM data was developed and tested using Landsat TM data. Classification accuracy results were compared to those from supervised and unsupervised classification. The STC provided the highest classification accuracy i.e., 83.9% correspondence between classified and referenced data compared to 44.2% and 34.5% for unsupervised and supervised classification respectively. Improvements in the classification process can be attributed to thematic stratification of the image data into spectrally homogenous (thematic) groups and step-by-step classification of the groups using supervised or unsupervised classification techniques. Supervised classification failed to classify 18% of the scene evidence that training data used did not adequately represent all of the variability in the data. Application of the procedure in drier miombo produced overall classification accuracy of 63%. This is much lower than that of wetter miombo. The results clearly demonstrate that digital classification of Landsat TM can be successfully implemented in the miombo without intensive fieldwork. Spatial characteristics of land cover change in agricultural and forested landscapes in central Malawi were analyzed for the period 1984 to 1995 spatial pattern analysis methods. Shifting cultivation areas, Agriculture in forested landscape, experienced highest rate of woodland cover fragmentation with mean patch size of closed woodland cover decreasing from 20ha to 7.5ha. Permanent bare (cropland and settlement) in intensive agricultural matrix landscapes increased 52% largely through the conversion of fallow areas. Protected National Park area remained fairly unchanged although closed woodland area increased by 4%, mainly from regeneration of open woodland. This study provided evidence that changes in spatial characteristics in the miombo differ with landscape. Land use change (i.e. conversion to cropland) is the primary driving force behind changes in landscape spatial patterns. Also, results revealed that exclusion of intense human use (i.e. cultivation and woodcutting) through regulations and/or fencing increased both closed woodland area (through regeneration of open woodland) and overall connectivity in the landscape. Spatial characteristics of land cover change were analyzed at locations in Malawi (wetter miombo) and Zimbabwe (drier miombo). Results indicate land cover dynamics differ both between and within case study sites. In communal areas in the Kasungu scene, land cover change is dominated by woodland fragmentation to open vegetation. Change in private commercial lands was dominantly expansion of bare (settlement and cropland) areas primarily at the expense of open vegetation (fallow land).

Qualitative properties of roasting defect beans and development of its classification methods by hyperspectral imaging technology.

PubMed

Cho, Jeong-Seok; Bae, Hyung-Jin; Cho, Byoung-Kwan; Moon, Kwang-Deog

2017-04-01

Qualitative properties of roasting defect coffee beans and their classification methods were studied using hyperspectral imaging (HSI). The roasting defect beans were divided into 5 groups: medium roasting (Cont), under developed (RD-1), over roasting (RD-2), interior under developed (RD-3), and interior scorching (RD-4). The following qualitative properties were assayed: browning index (BI), moisture content (MC), chlorogenic acid (CA), trigonelline (TG), and caffeine (CF) content. Their HSI spectra (1000-1700nm) were also analysed to develop the classification methods of roasting defect beans. RD-2 showed the highest BI and the lowest MC, CA, and TG content. The accuracy of classification model of partial least-squares discriminant was 86.2%. The most powerful wavelength to classify the defective beans was approximately 1420nm (related to OH bond). The HSI reflectance values at 1420nm showed similar tendency with MC, enabling the use of this technology to classify the roasting defect beans. Copyright © 2016. Published by Elsevier Ltd.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

PubMed

Chai, Rifai; Naik, Ganesh R; Nguyen, Tuan Nghia; Ling, Sai Ho; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-05-01

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

The Cross-Entropy Based Multi-Filter Ensemble Method for Gene Selection.

PubMed

Sun, Yingqiang; Lu, Chengbo; Li, Xiaobo

2018-05-17

The gene expression profile has the characteristics of a high dimension, low sample, and continuous type, and it is a great challenge to use gene expression profile data for the classification of tumor samples. This paper proposes a cross-entropy based multi-filter ensemble (CEMFE) method for microarray data classification. Firstly, multiple filters are used to select the microarray data in order to obtain a plurality of the pre-selected feature subsets with a different classification ability. The top N genes with the highest rank of each subset are integrated so as to form a new data set. Secondly, the cross-entropy algorithm is used to remove the redundant data in the data set. Finally, the wrapper method, which is based on forward feature selection, is used to select the best feature subset. The experimental results show that the proposed method is more efficient than other gene selection methods and that it can achieve a higher classification accuracy under fewer characteristic genes.

The effect of mental ill health on absence from work in different occupational classifications: analysis of routine data in the British Household Panel Survey.

PubMed

Whittaker, Will; Sutton, Matt; Macdonald, Sara; Maxwell, Margaret; Smith, Michael; Wilson, Philip; Morrison, Jill

2012-12-01

To investigate relationship of mental ill health to absence from work in different occupational classifications. Examined sickness absence, mental health (GHQ-12), physical health, job characteristics, and personal characteristics in 18 waves of the British Household Panel Survey. Overall sickness absence rate was 1.68%. Increased absence was associated with age greater than 45 years, female gender, lower occupational classification, and public-sector employers. Decreased absence was associated with part-time working. Scoring 4 or more on the General Health Questionnaire 12-item version (GHQ-12 caseness) was strongly associated with sickness absence. Public-sector employers had highest rates of sickness absence. GHQ-12 caseness had largest impact on absence in the public and nonprofit sectors, whereas physical health problems impacted more in the private sector. GHQ-12 caseness is strongly associated with increased absence in all classifications of occupations. Differences between sectors require further investigation.

Geology and forestry classification from ERTS-1 digital data

NASA Technical Reports Server (NTRS)

Lawrence, R. D.; Herzog, J. H.

1975-01-01

Computer classifications into seven and ten classes of two areas in central Oregon of interest to geology and forestry demonstrate the extraction of information from ERTS-1 data. The area around Newberry Caldera was classified into basalt, rhyolite obsidian, pumice flats, Newberry pumice, ponderosa pine, lodgepole pine, and water classes. The area around Mt. Washington was classified into two basalts, three forest, two clearcut, burn, snow, and water classes. Both also include an unclassified category. Significant details that cannot be extracted from photographic reconstitutions of the data emerge from these classifications, such as moraine locations and paleowind directions. Spectral signatures for the various rocks are comparable to those published elsewhere.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

Emerging Occupational Patterns in Australia in the Era of Globalisation and Rapid Technological Change: Implications for Education and Training.

ERIC Educational Resources Information Center

Maglen, Leo; Shah, Chandra

The effects of globalization and rapid technological change on emerging occupational patterns in Australia need to be understood in order to understand their implications for the effects on education and vocational training. Building on the classification scheme introduced by Robert Reich in his 1992 book, the Work of Nations, Australian…

Distinguishing Features of Emerging Adulthood: The Role of Self-Classification as an Adult

ERIC Educational Resources Information Center

Nelson, Larry J.; Barry, Carolyn McNamara

2005-01-01

Research reveals that most 18- to 25-year-old individuals do not consider themselves to be adults. This time period between adolescence and adulthood has been newly defined as emerging adulthood. The purpose of this study was to (a) attempt to identify perceived adults and (b) explore whether differences in adulthood criteria, achievement of those…

Factors Related to the Adoption of IT Emerging Technologies by Research and Non-Research Based Higher Education Institutions

ERIC Educational Resources Information Center

Then, Keri Ann; Amaria, Pesi

2013-01-01

This study examined the adoption of information technology (IT) emerging technology by higher education institutions with a focus on non-research and research based institutions categorized by Carnegie Mellon classifications that are members of EDUCAUSE, a higher education non-profit organization, whose mission is the use of IT in higher…

Criteria for phytoplasma 16Sr group/subgroup delineation and the need of a platform for proper registration of new groups and subgroups

USDA-ARS?s Scientific Manuscript database

As more phytoplasmas are discovered in emerging and re-emerging plant diseases worldwide, the scheme for classification of phytoplasmas into 16S rRNA gene RFLP (16Sr) groups and subgroups is experiencing an ongoing rapid expansion. Improper delineation or designation of new groups and subgroups can...

Effects of Three Depression Prevention Interventions on Risk for Depressive Disorder Onset in the Context of Depression Risk Factors

PubMed Central

Rohde, Paul; Stice, Eric; Gau, Jeff M.

2013-01-01

Study aims were to identify subgroups of adolescents with elevated depressive symptoms who had the highest likelihood of developing future major/minor depressive disorder on the basis of depression risk factors and participation in three depression prevention programs, with the goal of evaluating the preventive effect of indicated prevention interventions in the context of known risk factors. Adolescents (N = 341) with elevated depressive symptoms were randomized to one of four prevention intervention conditions (cognitive-behavioral group, supportive-expressive group, cognitive-behavioral bibliotherapy, educational brochure control). By 2-year follow-up, 14% showed onset of major/minor depressive disorders. Classification tree analysis (CTA) revealed that negative attributional style was the most important risk factor: youth with high scores showed a 4-fold increase in depression onset compared to youth who did not endorse this attributional style. For adolescents with negative attributional style, prevention condition emerged as the most important predictor: those receiving bibliotherapy showed a 5-fold reduction in depression disorder onset relative to adolescents in the three other intervention conditions. For adolescents who reported low negative attributional style scores, elevated levels of depressive symptoms at baseline emerged as the most potent predictor. Results implicate two key pathways to depression involving negative attributional style and elevated depressive symptoms in this population, and suggest that bibliotherapy may offset the risk conveyed by the most important depression risk factor in this sample. PMID:22932745

Perception of medical care systems and stress responses in preschoolers' caregivers at a pediatric emergency department in Taiwan.

PubMed

Kao, Jun-Kai; Cherng, Chian-Fang G; Tsai, Ru-Chiao; Tsao, Lon-Yen; Hung, Chin-Yi

2012-11-01

This study was undertaken to understand caregivers' perception of medical care systems and their stress responses throughout their stay with preschoolers at a pediatric emergency department (ED). Overall, 201 preschoolers' caregivers in pediatric ED were recruited in this study. They were asked to answer self-made questionnaires regarding the perception of medical care systems and their stress responses immediately before preschoolers' release. The results show that caregivers with a low education or low income level were prone to exhibit greater appreciation for the efficiency of medical care systems and greater degree of anxiety for their lack of treatment and care knowledge than those of caregivers with a high education or high income level. Interestingly, caregivers older than 38 years reported greater emotional responses and physical exhaustion than did younger caregivers. Immigrant caregivers had higher emotional reaction and lower concentration than did Taiwanese caregivers. Paradoxically, caregivers undergoing over 3-time pediatric ED visits for observation expressed stronger stress reactions as compared with caregivers with less observation experiences. Not surprisingly, caregivers reported the highest emotional responses when their preschoolers were diagnosed as having very urgent degree in triage classification. Finally, caregivers' perception of "lack of family support" and "lack of treatment and care knowledge" correlated positively with all aspects of the stress responses. These results indicate that attention should be paid to the specific psychological weakness and need for the caregivers with certain demographic characteristics by the medical team in pediatric ED.

Effects of three depression prevention interventions on risk for depressive disorder onset in the context of depression risk factors.

PubMed

Rohde, Paul; Stice, Eric; Gau, Jeff M

2012-12-01

Study aims were to identify subgroups of adolescents with elevated depressive symptoms who had the highest likelihood of developing future major/minor depressive disorder on the basis of depression risk factors and participation in three depression prevention programs, with the goal of evaluating the preventive effect of indicated prevention interventions in the context of known risk factors. Adolescents (N = 341) with elevated depressive symptoms were randomized to one of four prevention intervention conditions (cognitive-behavioral group, supportive-expressive group, cognitive-behavioral bibliotherapy, educational brochure control). By 2-year follow-up, 14% showed onset of major/minor depressive disorders. Classification tree analysis (CTA) revealed that negative attributional style was the most important risk factor: Youth with high scores showed a 4-fold increase in depression onset compared to youth who did not endorse this attributional style. For adolescents with negative attributional style, prevention condition emerged as the most important predictor: Those receiving bibliotherapy showed a 5-fold reduction in depression disorder onset relative to adolescents in the three other intervention conditions. For adolescents who reported low negative attributional style scores, elevated levels of depressive symptoms at baseline emerged as the most potent predictor. Results implicate two key pathways to depression involving negative attributional style and elevated depressive symptoms in this population, and suggest that bibliotherapy may offset the risk conveyed by the most important depression risk factor in this sample.

[Management of children with headache in a Pediatric Emergency Department before and after the introduction of the Second International Classification of Headache Disorders (ICHD-II)].

PubMed

Gioachin, Anna; Fiumana, Elisa; Tarocco, Anna; Verzola, Adriano; Forini, Elena; Guerra, Valentina; Salani, Manuela; Faggioli, Raffaella

2013-03-01

The aim of this study was to evaluate how the management of children admitted with headache to a Pediatric Emergency Department, was modified by the introduction of the Second International Classification of Headache Disorders ( ICHD-II) published in 2004. The complexity and average costs of the services provided to patients in 2002 and 2011 were compared. The results revealed a decrease in the number of tests performed and in-hospital admissions. However, tests were more complex, and an increase in requests of specialist advice was observed. We hypothesized that this change may be related to the introduction of ICHD-II, which suggests a more rational approach to the child with headache and a better use of hospital resources.

Spatial variation in hyperthermia emergency department visits among those with employer-based insurance in the United States - a case-crossover analysis.

PubMed

Saha, Shubhayu; Brock, John W; Vaidyanathan, Ambarish; Easterling, David R; Luber, George

2015-03-04

Predictions of intense heat waves across the United States will lead to localized health impacts, most of which are preventable. There is a need to better understand the spatial variation in the morbidity impacts associated with extreme heat across the country to prevent such adverse health outcomes. Hyperthermia-related emergency department (ED) visits were obtained from the Truven Health MarketScan(®) Research dataset for 2000-2010. Three measures of daily ambient heat were constructed using meteorological observations from the National Climatic Data Center (maximum temperature, heat index) and the Spatial Synoptic Classification. Using a time-stratified case crossover approach, odds ratio of hyperthermia-related ED visit were estimated for the three different heat measures. Random effects meta-analysis was used to combine the odds ratios for 94 Metropolitan Statistical Areas (MSA) to examine the spatial variation by eight latitude categories and nine U.S. climate regions. Examination of lags for all three temperature measures showed that the odds ratio of ED visit was statistically significant and highest on the day of the ED visit. For heat waves lasting two or more days, additional statistically significant association was observed when heat index and synoptic classification was used as the temperature measure. These results were insensitive to the inclusion of air pollution measures. On average, the maximum temperature on the day of an ED visit was 93.4°F in 'South' and 81.9°F in the 'Northwest' climatic regions of United States. The meta-analysis showed higher odds ratios of hyperthermia ED visit in the central and the northern parts of the country compared to the south and southwest. The results showed spatial variation in average temperature on days of ED visit and odds ratio for hyperthermia ED visits associated with extreme heat across United States. This suggests that heat response plans need to be customized for different regions and the potential role of hyperthermia ED visits in syndromic surveillance for extreme heat.

A Population-Based Cohort Study Evaluating Outcomes and Costs for Syncope Presentations to the Emergency Department.

PubMed

Sandhu, Roopinder K; Tran, Dat T; Sheldon, Robert S; Kaul, Padma

2018-02-01

This study sought to examine outcomes and costs of patients with syncope admitted and discharged from the emergency department (ED). ED visits for syncope are common, yet the impact on health care utilization is relatively unknown. A total of 51,831 consecutive patients presented to the ED with a primary diagnosis of syncope (International Classification of Diseases-9 code 780.2 and International Classification of Diseases-10 code R55) in Alberta, Canada from 2006 to 2014. Outcomes included 30-day syncope ED and hospital readmissions; 30-day and 1-year mortality; and annual inpatient, outpatient, physician, and drug costs, cumulative. Of adults presenting to the ED, 6.6% were hospitalized and discharged with a primary diagnosis of syncope (Cohort 1), 8.7% were hospitalized and discharged with a primary diagnosis other than syncope (Cohort 2), and 84.7% were discharged home with a syncope diagnosis (Cohort 3). The 30-day ED revisits for syncope varied from 1.2% (Cohort 2) to 2.4% (Cohort 1) (p < 0.001), and readmission rates were <1% among cohorts. Short- and long-term mortality rates were highest for Cohort 2 and lowest for Cohort 3 (30-day mortality: Cohort 1 of 1.2%, Cohort 2 of 5.2%, Cohort 3 of 0.4%; p < 0.001) (1-year mortality: Cohort 1 of 9.2%, Cohort 2 of 17.7%, Cohort 3 of 3.0%; p < 0.001). Total cost of syncope presentations was $530.6 million (Cohort 1: $75.3 million; $29,519/patient, Cohort 2: $138.1 million; $42,042/patient, Cohort 3: $317.3 million; $9,963/patient; p<0.001). Most patients with syncope presenting to the ED were discharged and had a favorable prognosis but overall costs were high compared with patients hospitalized. Further research is needed for cost-saving strategies across all cohorts. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Direct costs of emergency medical care: a diagnosis-based case-mix classification system.

PubMed

Baraff, L J; Cameron, J M; Sekhon, R

1991-01-01

To develop a diagnosis-based case mix classification system for emergency department patient visits based on direct costs of care designed for an outpatient setting. Prospective provider time study with collection of financial data from each hospital's accounts receivable system and medical information, including discharge diagnosis, from hospital medical records. Three community hospital EDs in Los Angeles County during selected times in 1984. Only direct costs of care were included: health care provider time, ED management and clerical personnel excluding registration, nonlabor ED expense including supplies, and ancillary hospital services. Indirect costs for hospitals and physicians, including depreciation and amortization, debt service, utilities, malpractice insurance, administration, billing, registration, and medical records were not included. Costs were derived by valuing provider time based on a formula using annual income or salary and fringe benefits, productivity and direct care factors, and using hospital direct cost to charge ratios. Physician costs were based on a national study of emergency physician income and excluded practice costs. Patients were classified into one of 216 emergency department groups (EDGs) on the basis of the discharge diagnosis, patient disposition, age, and the presence of a limited number of physician procedures. Total mean direct costs ranged from $23 for follow-up visit to $936 for trauma, admitted, with critical care procedure. The mean total direct costs for the 16,771 nonadmitted patients was $69. Of this, 34% was for ED costs, 45% was for ancillary service costs, and 21% was for physician costs. The mean total direct costs for the 1,955 admitted patients was $259. Of this, 23% was for ED costs, 63% was for ancillary service costs, and 14% was for physician costs. Laboratory and radiographic services accounted for approximately 85% of all ancillary service costs and 38% of total direct costs for nonadmitted patients versus 80% of ancillary service costs and 51% of total direct costs for admitted patients. We have developed a diagnosis-based case mix classification system for ED patient visits based on direct costs of care designed for an outpatient setting which, unlike diagnosis-related groups, includes the measurement of time-based cost for physician and nonphysician services. This classification system helps to define direct costs of hospital and physician emergency services by type of patient.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework.

PubMed

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

2016-02-01

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice. Copyright © 2015 Elsevier Inc. All rights reserved.

Adult Status Epilepticus: A Review of the Prehospital and Emergency Department Management

PubMed Central

Billington, Michael; Kandalaft, Osama R.; Aisiku, Imoigele P.

2016-01-01

Seizures are a common presentation in the prehospital and emergency department setting and status epilepticus represents an emergency neurologic condition. The classification and various types of seizures are numerous. The objectives of this narrative literature review focuses on adult patients with a presentation of status epilepticus in the prehospital and emergency department setting. In summary, benzodiazepines remain the primary first line therapeutic agent in the management of status epilepticus, however, there are new agents that may be appropriate for the management of status epilepticus as second- and third-line pharmacological agents. PMID:27563928

Vulnerable land ecosystems classification using spatial context and spectral indices

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martín, Consuelo; Marcello, Javier

2017-10-01

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Validity of the American Board of Orthodontics Discrepancy Index and the Peer Assessment Rating Index for comprehensive evaluation of malocclusion severity.

PubMed

Liu, S; Oh, H; Chambers, D W; Baumrind, S; Xu, T

2017-08-01

To assess the validity of the American Board of Orthodontics Discrepancy Index (ABO-DI) and Peer Assessment Rating (PAR) Index in evaluating malocclusion severity in Chinese orthodontic patients. A stratified random sample of 120 orthodontic patients based on Angle classification was collected from six university orthodontic centres. Sixty-nine orthodontists rated malocclusion severity on a five-point scale by assessing a full set of pre-treatment records for each case and listed reasons for their decision. Their judgement was then compared with ABO-DI and PAR scores determined by three calibrated examiners. Excellent interexaminer reliability of clinician judgement, ABO-DI and PAR index was demonstrated by the Intraclass Correlation Coefficient (rho= 0.995, 0.990 and 0.964, respectively). Both the ABO-DI and US-PAR index showed good correlation with clinician judgement (r=.700 and r=.707, respectively). There was variability among the different Angle classifications: the ABO-DI showed the highest correlation with clinician judgement in Class II patients (r=.780), whereas the US-PAR index showed the highest correlation with clinician judgement in Class III patients (r=.710). Both indices demonstrated the lowest correlations with clinician judgement in Class I patients. With strong interexaminer agreement, the panel consensus was used for validating the ABO-DI and US-PAR index for malocclusion severity. Overall, the ABO-DI and US-PAR index were reliable for measuring malocclusion severity with significantly variable weightings for different Angle classifications. Further modification of the indices for different Angle classification may be indicated. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Incidence and epidemiology of tibial shaft fractures.

PubMed

Larsen, Peter; Elsoe, Rasmus; Hansen, Sandra Hope; Graven-Nielsen, Thomas; Laessoe, Uffe; Rasmussen, Sten

2015-04-01

The literature lacks recent population-based epidemiology studies of the incidence, trauma mechanism and fracture classification of tibial shaft fractures. The purpose of this study was to provide up-to-date information on the incidence of tibial shaft fractures in a large and complete population and report the distribution of fracture classification, trauma mechanism and patient baseline demographics. Retrospective reviews of clinical and radiological records. A total of 196 patients were treated for 198 tibial shaft fractures in the years 2009 and 2010. The mean age at time of fracture was 38.5 (21.2SD) years. The incidence of tibial shaft fracture was 16.9/100,000/year. Males have the highest incidence of 21.5/100,000/year and present with the highest frequency between the age of 10 and 20, whereas women have a frequency of 12.3/100,000/year and have the highest frequency between the age of 30 and 40. AO-type 42-A1 was the most common fracture type, representing 34% of all tibial shaft fractures. The majority of tibial shaft fractures occur during walking, indoor activity and sports. The distribution among genders shows that males present a higher frequency of fractures while participating in sports activities and walking. Women present the highest frequency of fractures while walking and during indoor activities. This study shows an incidence of 16.9/100,000/year for tibial shaft fractures. AO-type 42-A1 was the most common fracture type, representing 34% of all tibial shaft fractures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Formalization of the classification pattern: survey of classification modeling in information systems engineering.

PubMed

Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

2018-01-01

Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus to the ISE literature. The literature survey follows the evolution of ISE's understanding of how to formalize the classification pattern. The various proposals are assessed using the classical example of classification; the Linnaean taxonomy formalized using powersets as a benchmark for formal expressiveness. The broad conclusion of the survey is that (1) the ISE community is currently in the early stages of the process of understanding how to formalize the classification pattern, particularly in the requirements for expressiveness exemplified by powersets, and (2) that there is an opportunity to intervene and speed up the process of adoption by clarifying this expressiveness. Given the central place that the classification pattern has in domain modeling, this intervention has the potential to lead to significant improvements.

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

THE MAGNETIC CLASSIFICATION OF SOLAR ACTIVE REGIONS 1992–2015

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

Jaeggli, S. A.; Norton, A. A., E-mail: sarah.jaeggli@nasa.gov

The purpose of this Letter is to address a blindspot in our knowledge of solar active region (AR) statistics. To the best of our knowledge, there are no published results showing the variation of the Mount Wilson magnetic classifications as a function of solar cycle based on modern observations. We show statistics for all ARs reported in the daily Solar Region Summary from 1992 January 1 to 2015 December 31. We find that the α and β class ARs (including all sub-groups, e.g., βγ, βδ) make up fractions of approximately 20% and 80% of the sample, respectively. This fraction ismore » relatively constant during high levels of activity; however, an increase in the α fraction to about 35% and and a decrease in the β fraction to about 65% can be seen near each solar minimum and are statistically significant at the 2σ level. Over 30% of all ARs observed during the years of solar maxima were appended with the classifications γ and/or δ, while these classifications account for only a fraction of a percent during the years near the solar minima. This variation in the AR types indicates that the formation of complex ARs may be due to the pileup of frequent emergence of magnetic flux during solar maximum, rather than the emergence of complex, monolithic flux structures.« less

Towards automatic lithological classification from remote sensing data using support vector machines

NASA Astrophysics Data System (ADS)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

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.

Shifting syndromes: Sex chromosome variations and intersex classifications

PubMed Central

Griffiths, David Andrew

2018-01-01

The 2006 ‘Consensus statement on management of intersex disorders’ recommended moving to a new classification of intersex variations, framed in terms of ‘disorders of sex development’ or DSD. Part of the rationale for this change was to move away from associations with gender, and to increase clarity by grounding the classification system in genetics. While the medical community has largely accepted the move, some individuals from intersex activist communities have condemned it. In addition, people both inside and outside the medical community have disagreed about what should be covered by the classification system, in particular whether sex chromosome variations and the related diagnoses of Turner and Klinefelter’s syndromes should be included. This article explores initial descriptions of Turner and Klinefelter’s syndromes and their subsequent inclusion in intersex classifications, which were increasingly grounded in scientific understandings of sex chromosomes that emerged in the 1950s. The article questions the current drive to stabilize and ‘sort out’ intersex classifications through a grounding in genetics. Alternative social and historical definitions of intersex – such as those proposed by the intersex activists – have the potential to do more justice to the lived experience of those affected by such classifications and their consequences. PMID:29424285

Continuous robust sound event classification using time-frequency features and deep learning

PubMed Central

Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification. PMID:28892478

Continuous robust sound event classification using time-frequency features and deep learning.

PubMed

McLoughlin, Ian; Zhang, Haomin; Xie, Zhipeng; Song, Yan; Xiao, Wei; Phan, Huy

2017-01-01

The automatic detection and recognition of sound events by computers is a requirement for a number of emerging sensing and human computer interaction technologies. Recent advances in this field have been achieved by machine learning classifiers working in conjunction with time-frequency feature representations. This combination has achieved excellent accuracy for classification of discrete sounds. The ability to recognise sounds under real-world noisy conditions, called robust sound event classification, is an especially challenging task that has attracted recent research attention. Another aspect of real-word conditions is the classification of continuous, occluded or overlapping sounds, rather than classification of short isolated sound recordings. This paper addresses the classification of noise-corrupted, occluded, overlapped, continuous sound recordings. It first proposes a standard evaluation task for such sounds based upon a common existing method for evaluating isolated sound classification. It then benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end. Results are reported for each tested system using the new task, to provide the first analysis of their performance for continuous sound event detection. In addition it proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification.

China's research status in emergency medicine: a 15-year survey of literature.

PubMed

Wang, Weiwei; Jiang, Ting; Li, Chunyu; Chen, Jun; Cao, Kejiang; Qi, Lian-wen; Li, Ping; Zhu, Wei; Zhu, Baoli; Chen, Yan

2016-03-01

To investigate the research status of emergency medicine in China through literature search of international emergency medicine journals and retrospectively compare the outputs of emergency medicine articles of the 3 major regions of China-Mainland (ML), Taiwan (TW), and Hong Kong (HK). Emergency medicine journals were selected category from Science Citation Index Expand. Articles from the ML, TW, and HK were retrieved from PubMed database. The total number of articles, publication types, research contents, impact factors (IF), and articles published in each journal were conducted for quantity and quality comparisons. A total of 1760 articles from 19 emergency medicine journals were searched, of which 395 were from ML, 1210 from TW, and 155 from HK. Accumulated IF of articles from TW (2451.109) was much higher than that of ML (851.832) and HK (328.579), whereas the average IF of articles from TW (2.02) was the lowest. The number of case reports was the highest, which was, 69 from ML, 637 from TW, and 25 from HK, respectively. Although emergency medicine was involved with multiple organs and multiple systems, the reports of trauma accounted for 25% of the research contents. The total number of articles from both China and the rest of the world increased significantly from 2000 to 2014, especially ML. The total number of articles from TW was still much more than that of ML and HK, whereas the quality of articles from TW was not as good as ML and HK. Case report had the highest share of publication types, whereas the proportions of meta-analysis and observational study were the lowest. As for research contents, the proportion of trauma was still the highest. Copyright © 2015 Elsevier Inc. All rights reserved.

[Evaluation of new and emerging health technologies. Proposal for classification].

PubMed

Prados-Torres, J D; Vidal-España, F; Barnestein-Fonseca, P; Gallo-García, C; Irastorza-Aldasoro, A; Leiva-Fernández, F

2011-01-01

Review and develop a proposal for the classification of health technologies (HT) evaluated by the Health Technology Assessment Agencies (HTAA). Peer review of AETS of the previous proposed classification of HT. Analysis of their input and suggestions for amendments. Construction of a new classification. Pilot study with physicians. Andalusian Public Health System. Spanish HTAA. Experts from HTAA. Tutors of family medicine residents. HT Update classification previously made by the research team. Peer review by Spanish HTAA. Qualitative and quantitative analysis of responses. Construction of a new and pilot study based on 12 evaluation reports of the HTAA. We obtained 11 thematic categories that are classified into 6 major head groups: 1, prevention technology; 2, diagnostic technology; 3, therapeutic technologies; 4, diagnostic and therapeutic technologies; 5, organizational technology, and 6, knowledge management and quality of care. In the pilot there was a good concordance in the classification of 8 of the 12 reports reviewed by physicians. Experts agree on 11 thematic categories of HT. A new classification of HT with double entry (Nature and purpose of HT) is proposed. APPLICABILITY: According to experts, the classification of the work of the HTAA may represent a useful tool to transfer and manage knowledge. Moreover, an adequate classification of the HTAA reports would help clinicians and other potential users to locate them and this can facilitate their dissemination. Copyright © 2010 SECA. Published by Elsevier Espana. All rights reserved.

Canonical Sectors and Evolution of Firms in the US Stock Markets

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Chachra, Ricky; Alemi, Alexander; Ginsparg, Paul; Sethna, James

2015-03-01

In this work, we show how unsupervised machine learning can provide a more objective and comprehensive broad-level sector decomposition of stocks. Classification of companies into sectors of the economy is important for macroeconomic analysis, and for investments into the sector-specific financial indices and exchange traded funds (ETFs). Historically, these major industrial classification systems and financial indices have been based on expert opinion and developed manually. Our method, in contrast, produces an emergent low-dimensional structure in the space of historical stock price returns. This emergent structure automatically identifies ``canonical sectors'' in the market, and assigns every stock a participation weight into these sectors. Furthermore, by analyzing data from different periods, we show how these weights for listed firms have evolved over time. This work was partially supported by NSF Grants DMR 1312160, OCI 0926550 and DGE-1144153 (LXH).

Assessment and risk classification protocol for patients in emergency units1

PubMed Central

Silva, Michele de Freitas Neves; Oliveira, Gabriela Novelli; Pergola-Marconato, Aline Maino; Marconato, Rafael Silva; Bargas, Eliete Boaventura; Araujo, Izilda Esmenia Muglia

2014-01-01

Objective to develop, validate the contents and verify the reliability of a risk classification protocol for an Emergency Unit. Method the content validation was developed in a University Hospital in a country town located in the state of Sao Paulo and was carried out in two stages: the first with the individual assessment of specialists and the second with the meeting between the researchers and the specialists. The use of the protocol followed a specific guide. Concerning reliability, the concordance or equivalent method among observers was used. Results the protocol developed showed to have content validity and, after the suggested changes were made, there were excellent results concerning reliability. Conclusion the assistance flow chart was shown to be easy to use, and facilitate the search for the complaint in each assistance priority. PMID:26107828

Mapping permafrost in the boreal forest with Thematic Mapper satellite data

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Strong, L. L.; Card, D. H.

1986-01-01

A geographic data base incorporating Landsat TM data was used to develop and evaluate logistic discriminant functions for predicting the distribution of permafrost in a boreal forest watershed. The data base included both satellite-derived information and ancillary map data. Five permafrost classifications were developed from a stratified random sample of the data base and evaluated by comparison with a photo-interpreted permafrost map using contingency table analysis and soil temperatures recorded at sites within the watershed. A classification using a TM thermal band and a TM-derived vegetation map as independent variables yielded the highest mapping accuracy for all permafrost categories.

Rating the Accessibility of Library Tutorials from Leading Research Universities

ERIC Educational Resources Information Center

Clossen, Amanda; Proces, Paul

2017-01-01

Video and Web-based tutorials created by libraries from 71 public universities designated by the Carnegie Classification as having the Highest Research Activity (R1) were reviewed for accessibility and usability by disabled people. The results of this review indicate that a large portion of library tutorial content meets neither the minimum legal…

The investigation of classification methods of high-resolution imagery

Treesearch

Tracey S. Frescino; Gretchen G. Moisen; Larry DeBlander; Michel Guerin

2007-01-01

As remote-sensing technology advances, high-resolution imagery, such as Quickbird and photography from the National Agriculture Imagery Program (NAIP), is becoming more readily available for use in forestry applications. Quickbird imagery is currently the highest resolution imagery commercially available. It consists of 2.44-m (8-ft) resolution multispectral bands...

Study on Mine Emergency Mechanism based on TARP and ICS

NASA Astrophysics Data System (ADS)

Xi, Jian; Wu, Zongzhi

2018-01-01

By analyzing the experiences and practices of mine emergency in China and abroad, especially the United States and Australia, normative principle, risk management principle and adaptability principle of constructing mine emergency mechanism based on Trigger Action Response Plans (TARP) and Incident Command System (ICS) are summarized. Classification method, framework, flow and subject of TARP and ICS which are suitable for the actual situation of domestic mine emergency are proposed. The system dynamics model of TARP and ICS is established. The parameters such as evacuation ratio, response rate, per capita emergency capability and entry rate of rescuers are set up. By simulating the operation process of TARP and ICS, the impact of these parameters on the emergency process are analyzed, which could provide a reference and basis for building emergency capacity, formulating emergency plans and setting up action plans in the emergency process.

An Evolving Digital Telecommunications Industry and Its Impact on the Operational Environment of the Nationwide Emergency Telecommunication System (NETS).

DTIC Science & Technology

1985-03-01

005 4-, /nImII 1 liIm ulnli iiliiii I I UNCLASSIFIED SECURITY CLASSIFICATION OF TNIS PAGE ("on Data Bnter6t0 REPORT DOCUMENTATION PAGE READ...II different from ControllIng Office) 15. SECURITY CLASS (of thin report) Unclassified IS. OECLASSIFICATION/ DOWNGRADING SCMEDULE 1. DISTRIBUTION...EDITION OF I NOV 65 IS OBSOLETE UNCLASSIFIED N0102LF-0146601 1 UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Dote wrteed) ,IA UNCLASSIFIED

Prognosis elements in surgical treatment of complicated umbilical hernia in patients with liver cirrhosis.

PubMed

Banu, P; Popa, F; Constantin, V D; Bălălău, C; Nistor, M

2013-09-15

The surgical treatment of umbilical hernia in cirrhosis patients raises special management challenges. The attitude upon the repair of these hernias varies from expectancy or elective treatment in early stages of the disease to the surgical treatment only if complications occur. We have assessed 22 consecutive cases of cirrhosis patients treated for complicated umbilical hernia in the Surgical Department of "Sf. Pantelimon" Emergency Hospital in Bucharest between January 2008 and December 2012. The patients' stratification was done in stages of liver disease based upon Child-Pugh classification. Complications that required emergency repair were the following: strangulation, incarceration and hernia rupture. The postoperative complications were ordered in five grades of severity based upon Clavien classification. The severity of the complications was higher in advanced stages of liver cirrhosis, Child B and C. There were 5 deaths representing 22,7%, four of them in patients with Child C disease stage. The incidence of morbidity and mortality after umbilical hernia repair in emergencies increases in advanced stages of liver cirrhosis. It is advisable to prevent complications occurrence and perform surgical repair of umbilical hernia in elective condition.

Interobserver reliability of the young-burgess and tile classification systems for fractures of the pelvic ring.

PubMed

Koo, Henry; Leveridge, Mike; Thompson, Charles; Zdero, Rad; Bhandari, Mohit; Kreder, Hans J; Stephen, David; McKee, Michael D; Schemitsch, Emil H

2008-07-01

The purpose of this study was to measure interobserver reliability of 2 classification systems of pelvic ring fractures and to determine whether computed tomography (CT) improves reliability. The reliability of several radiographic findings was also tested. Thirty patients taken from a database at a Level I trauma facility were reviewed. For each patient, 3 radiographs (AP pelvis, inlet, and outlet) and CT scans were available. Six different reviewers (pelvic and acetabular specialist, orthopaedic traumatologist, or orthopaedic trainee) classified the injury according to Young-Burgess and Tile classification systems after reviewing plain radiographs and then after CT scans. The Kappa coefficient was used to determine interobserver reliability of these classification systems before and after CT scan. For plain radiographs, overall Kappa values for the Young-Burgess and Tile classification systems were 0.72 and 0.30, respectively. For CT scan and plain radiographs, the overall Kappa values for the Young-Burgess and Tile classification systems were 0.63 and 0.33, respectively. The pelvis/acetabular surgeons demonstrated the highest level of agreement using both classification systems. For individual questions, the addition of CT did significantly improve reviewer interpretation of fracture stability. The pre-CT and post-CT Kappa values for fracture stability were 0.59 and 0.93, respectively. The CT scan can improve the reliability of assessment of pelvic stability because of its ability to identify anatomical features of injury. The Young-Burgess system may be optimal for the learning surgeon. The Tile classification system is more beneficial for specialists in pelvic and acetabular surgery.

Review of in situ and invasive penile squamous cell carcinoma and associated non-neoplastic dermatological conditions.

PubMed

Downes, Michelle R

2015-05-01

Penile carcinoma is a rare genitourinary malignancy in North America and Europe with highest rates recorded in South America, Africa and Asia. Recent classifications have refined the terminology used in classifying intraepithelial/in situ lesions and additionally newer entities have been recognised in the invasive category. While increasing recognition of a bimodal pathway of penile carcinogenesis has facilitated understanding and classification of these tumours, handling and subtyping of penile malignancies presents a challenge to the reporting pathologist, in part due to their rarity. This article reviews the terminology and classification of in situ and invasive carcinomas and their relationship to human papilloma virus status. In addition, associated non-neoplastic dermatological conditions of relevance and appropriate ancillary investigations will be addressed. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Unsupervised Feature Learning for Heart Sounds Classification Using Autoencoder

NASA Astrophysics Data System (ADS)

Hu, Wei; Lv, Jiancheng; Liu, Dongbo; Chen, Yao

2018-04-01

Cardiovascular disease seriously threatens the health of many people. It is usually diagnosed during cardiac auscultation, which is a fast and efficient method of cardiovascular disease diagnosis. In recent years, deep learning approach using unsupervised learning has made significant breakthroughs in many fields. However, to our knowledge, deep learning has not yet been used for heart sound classification. In this paper, we first use the average Shannon energy to extract the envelope of the heart sounds, then find the highest point of S1 to extract the cardiac cycle. We convert the time-domain signals of the cardiac cycle into spectrograms and apply principal component analysis whitening to reduce the dimensionality of the spectrogram. Finally, we apply a two-layer autoencoder to extract the features of the spectrogram. The experimental results demonstrate that the features from the autoencoder are suitable for heart sound classification.

High-throughput automatic defect review for 300mm blank wafers with atomic force microscope

NASA Astrophysics Data System (ADS)

Zandiatashbar, Ardavan; Kim, Byong; Yoo, Young-kook; Lee, Keibock; Jo, Ahjin; Lee, Ju Suk; Cho, Sang-Joon; Park, Sang-il

2015-03-01

While feature size in lithography process continuously becomes smaller, defect sizes on blank wafers become more comparable to device sizes. Defects with nm-scale characteristic size could be misclassified by automated optical inspection (AOI) and require post-processing for proper classification. Atomic force microscope (AFM) is known to provide high lateral and the highest vertical resolution by mechanical probing among all techniques. However, its low throughput and tip life in addition to the laborious efforts for finding the defects have been the major limitations of this technique. In this paper we introduce automatic defect review (ADR) AFM as a post-inspection metrology tool for defect study and classification for 300 mm blank wafers and to overcome the limitations stated above. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for nm-scale defect review and classification.

Quantitative EEG features selection in the classification of attention and response control in the children and adolescents with attention deficit hyperactivity disorder.

PubMed

Bashiri, Azadeh; Shahmoradi, Leila; Beigy, Hamid; Savareh, Behrouz A; Nosratabadi, Masood; N Kalhori, Sharareh R; Ghazisaeedi, Marjan

2018-06-01

Quantitative EEG gives valuable information in the clinical evaluation of psychological disorders. The purpose of the present study is to identify the most prominent features of quantitative electroencephalography (QEEG) that affect attention and response control parameters in children with attention deficit hyperactivity disorder. The QEEG features and the Integrated Visual and Auditory-Continuous Performance Test ( IVA-CPT) of 95 attention deficit hyperactivity disorder subjects were preprocessed by Independent Evaluation Criterion for Binary Classification. Then, the importance of selected features in the classification of desired outputs was evaluated using the artificial neural network. Findings uncovered the highest rank of QEEG features in each IVA-CPT parameters related to attention and response control. Using the designed model could help therapists to determine the existence or absence of defects in attention and response control relying on QEEG.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Diagnosis of diabetes diseases using an Artificial Immune Recognition System2 (AIRS2) with fuzzy K-nearest neighbor.

PubMed

Chikh, Mohamed Amine; Saidi, Meryem; Settouti, Nesma

2012-10-01

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

User embracement with risk classification in an emergency care unit: an evaluative study.

PubMed

Hermida, Patrícia Madalena Vieira; Nascimento, Eliane Regina Pereira do; Echevarría-Guanilo, Maria Elena; Brüggemann, Odaléa Maria; Malfussi, Luciana Bihain Hagemann de

2018-01-01

Objective Describing the evaluation of the Structure, Process and Outcome of User Embracement with Risk Classification of an Emergency Care Unit from the perspective of physicians and nurses. Method An evaluative, descriptive, quantitative study developed in Santa Catarina. Data were collected using a validated and adapted instrument consisting of 21 items distributed in the dimensions of Structure (facilities), Process (activities and relationships in providing care) and Outcome (care effects). In the analysis, descriptive statistics and the Mean Ranking and Mean Score calculations were applied. Results The sample consisted of 37 participants. From the 21 evaluated items, 11 (52.4%) had a Mean Ranking between 3 and 4, and none of them reached the maximum ranking (5 points). "Prioritization of severe cases" and "Primary care according to the severity of the case" reached a higher Mean Ranking (4.5), while "Flowchart discussion" had the lowest Ranking (2.1). The dimensions of Structure, Process and Outcome reached mean scores of 23.9, 21.9 and 25.5, respectively, indicating a Precarious evaluation (17.5 to 26.1 points). Conclusion User Embracement with Risk Classification is precarious, especially regarding the Process which obtained a lower satisfaction level from the participants.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Incidence of sports and recreation related injuries resulting in hospitalization in Wisconsin in 2000.

PubMed

Dempsey, R L; Layde, P M; Laud, P W; Guse, C E; Hargarten, S W

2005-04-01

To describe the incidence and patterns of sports and recreation related injuries resulting in inpatient hospitalization in Wisconsin. Although much sports and recreation related injury research has focused on the emergency department setting, little is known about the scope or characteristics of more severe sports injuries resulting in hospitalization. The Wisconsin Bureau of Health Information (BHI) maintains hospital inpatient discharge data through a statewide mandatory reporting system. The database contains demographic and health information on all patients hospitalized in acute care non-federal hospitals in Wisconsin. The authors developed a classification scheme based on the International Classification of Diseases External cause of injury code (E code) to identify hospitalizations for sports and recreation related injuries from the BHI data files (2000). Due to the uncertainty within E codes in specifying sports and recreation related injuries, the authors used Bayesian analysis to model the incidence of these types of injuries. There were 1714 (95% credible interval 1499 to 2022) sports and recreation-related injury hospitalizations in Wisconsin in 2000 (32.0 per 100,000 population). The most common mechanisms of injury were being struck by/against an object in sports (6.4 per 100,000 population) and pedal cycle riding (6.2 per 100,000). Ten to 19 year olds had the highest rate of sports and recreation related injury hospitalization (65.3 per 100,000 population), and males overall had a rate four times higher than females. Over 1700 sports and recreation related injuries occurred in Wisconsin in 2000 that were treated during an inpatient hospitalization. Sports and recreation activities result in a substantial number of serious, as well as minor injuries. Prevention efforts aimed at reducing injuries while continuing to promote participation in physical activity for all ages are critical.

Remote sensing and environment in the study of the malaria vector Anopheles gambiae in Mali

NASA Astrophysics Data System (ADS)

Rian, Sigrid Katrine Eivindsdatter

The malaria mosquito Anopheles gambiae is the most important vector for the most devastating form of human malaria, the parasite Plasmodium falciparum. In-depth knowledge of the vector's history and environmental preferences is essential in the pursuit of new malaria mitigation strategies. Research was conducted in Mali across a range of habitats occupied by the vector, focusing on three identified chromosomal forms in the mosquito complex. The development of a 500-m landcover classification map was carried out using MODIS satellite imagery and extensive ground survey. The resulting product has the highest resolution and is the most up-to-date and most extensively ground-surveyed among land-cover maps for the study region. The new landcover classification product is a useful tool in the mapping of the varying ecological preferences of the different An. gambiae chromosomal forms. Climate and vegetation characteristics and their relationship to chromosomal forms were investigated further along a Southwest-Northeast moisture gradient in Mali. This research demonstrates particular ecological preferences of each chromosomal form, and gives a detailed examination of particular vegetation structural and climatological patterns across the study region. A key issue in current research into the population structure of An. gambiae is speciation and evolution in the complex, as an understanding of the mechanisms of change can help in the development of new mitigation strategies. A historical review of the paleoecology, archaeology, and other historical sources intended to shed light on the evolutionary history of the vector is presented. The generally held assumption that the current breed of An. gambiae emerged in the rainforest is called into question and discussed within the framework of paleoenvironment and human expansions in sub-Saharan West Africa.

Sensitivity and specificity of the Manchester Triage System in risk prioritization of patients with acute myocardial infarction who present with chest pain.

PubMed

Nishi, Fernanda A; Polak, Catarina; Cruz, Diná de Almeida Lopes Monteiro da

2018-05-01

The purpose of the Manchester Triage System is to clinically prioritize each patient seeking care in an emergency department. Patients with suspected acute myocardial infarction who have typical symptoms including chest pain should be classified in the highest priority groups, requiring immediate medical assistance or care within 10 min. As such, the Manchester Triage System should present adequate sensitivity and specificity. This study estimated the sensitivity and specificity of the Manchester Triage System in the triage of patients with chest pain related to the diagnosis of acute myocardial infarction, and the associations between the performance of the Manchester Triage System and selected variables. This was an observational, analytical, cross-sectional, retrospective study. The sensitivity and specificity of the Manchester Triage System were estimated by verifying the triage classification received by these patients and their established medical diagnoses. The sample was composed of 10,087 triage episodes, in which 139 (1.38%) patients had a diagnosis of acute myocardial infarction. In 49 episodes, confirmation of medical diagnosis was not possible. The estimated sensitivity of the Manchester Triage System was 44.60% (36.18-53.27%) and the estimated specificity was 91.30% (90.73-91.85%). Of the 10,038 episodes in which the diagnosis of acute myocardial infarction was confirmed or excluded, 938 patients (9.34%) received an incorrect classification - undertriage or overtriage. This study showed that the specificity of the Manchester Triage System was very good. However, the low sensitivity based on the Manchester Triage System indicated that patients in high priority categories were undertriaged, leading to longer wait times and associated increased risks of adverse events.

Calibration and Validation Plan for the L2A Processor and Products of the SENTINEL-2 Mission

NASA Astrophysics Data System (ADS)

Main-Knorn, M.; Pflug, B.; Debaecker, V.; Louis, J.

2015-04-01

The Copernicus programme, is a European initiative for the implementation of information services based on observation data received from Earth Observation (EO) satellites and ground based information. In the frame of this programme, ESA is developing the Sentinel-2 optical imaging mission that will deliver optical data products designed to feed downstream services mainly related to land monitoring, emergency management and security. To ensure the highest quality of service, ESA sets up the Sentinel-2 Mission Performance Centre (MPC) in charge of the overall performance monitoring of the Sentinel-2 mission. TPZ F and DLR have teamed up in order to provide the best added-value support to the MPC for calibration and validation of the Level-2A processor (Sen2Cor) and products. This paper gives an overview over the planned L2A calibration and validation activities. Level-2A processing is applied to Top-Of-Atmosphere (TOA) Level-1C ortho-image reflectance products. Level-2A main output is the Bottom-Of-Atmosphere (BOA) corrected reflectance product. Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification (SC) map with Quality Indicators for cloud and snow probabilities. Level-2A BOA, AOT and WV outputs are calibrated and validated using ground-based data of automatic operating stations and data of in-situ campaigns. Scene classification is validated by the visual inspection of test datasets and cross-sensor comparison, supplemented by meteorological data, if available. Contributions of external in-situ campaigns would enlarge the reference dataset and enable extended validation exercise. Therefore, we are highly interested in and welcome external contributors.

Accuracy of Diagnosis Codes to Identify Febrile Young Infants Using Administrative Data

PubMed Central

Aronson, Paul L.; Williams, Derek J.; Thurm, Cary; Tieder, Joel S.; Alpern, Elizabeth R.; Nigrovic, Lise E.; Schondelmeyer, Amanda C.; Balamuth, Fran; Myers, Angela L.; McCulloh, Russell J.; Alessandrini, Evaline A.; Shah, Samir S.; Browning, Whitney L.; Hayes, Katie L.; Feldman, Elana A.; Neuman, Mark I.

2015-01-01

Background Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development. Objective Determine the most accurate International Classification of Diseases, 9th revision (ICD-9) diagnosis coding strategies for identification of febrile infants. Design Retrospective cross-sectional study. Setting Eight emergency departments in the Pediatric Health Information System. Patients Infants age < 90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from one of four ICD-9 diagnosis code groups: 1) discharge diagnosis of fever, 2) admission diagnosis of fever without discharge diagnosis of fever, 3) discharge diagnosis of serious infection without diagnosis of fever, and 4) no diagnosis of fever or serious infection. Exposure The ICD-9 diagnosis code groups were compared in four case-identification algorithms to a reference standard of fever ≥ 100.4°F documented in the medical record. Measurements Algorithm predictive accuracy was measured using sensitivity, specificity, negative and positive predictive values. Results Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3). Conclusions A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though under-classification of patients is a potential limitation. PMID:26248691

Demographics and Fellowship Training of Residency Leadership in EM: A Descriptive Analysis.

PubMed

Greenstein, Josh; Hardy, Ross; Chacko, Jerel; Husain, Abbas

2017-01-01

Emergency medicine (EM) fellowships are becoming increasingly numerous, and there is a growing trend among EM residents to pursue postgraduate fellowship training. Scant data have been published on the prevalence of postgraduate training among emergency physicians. We aimed to describe the prevalence and regional variation of fellowships among EM residency leadership. We conducted an online anonymous survey that was sent to the Council of EM Residency Directors (CORD) membership in October 2014. The survey was a brief questionnaire, which inquired about fellowship, secondary board certification, gender, and length in a leadership position of each member of its residency leadership. We separated the responses to the survey into four different geographic regions. The geographic regions were defined by the same classification used by the National Resident Matching Program (NRMP). We defined residency leadership as program director (PD), associate PD and assistant PD. Residencies that did not complete the survey were then individually contacted to encourage completion. The survey was initially piloted for ease of use and understanding of the questions with a select few EM PDs. We obtained responses from 145 of the 164 Accrediting Council for Graduate Medical Education-accredited EM residencies (88%). The fellowship prevalence among PDs, associate PDs, and assistant PDs was 21.4%, 20.3%, and 24.9% respectively. The most common fellowship completed was a fellowship in toxicology. Secondary board certification among PDs, associate PDs, and assistant PDs was 9.7%, 4.8%, and 2.9% respectively. Eighty-two percent of PDs have at least five years in residency leadership. Seventy-six percent of PDs were male, and there was a near-even split of gender among associate PDs and assistant PDs. The Western region had the highest percentage of fellowship and or secondary board certification among all levels of residency leadership. There is a low prevalence of fellowship training and secondary board certification among EM residency leadership, with the most common being toxicology. Assistant PDs, the majority of whom had less than five years residency leadership experience, had the highest percentage of fellowship training. There may be a regional variation in the percentage of residency leadership completing postgraduate training.

Wireless Emergency Alerts (WEA) Cybersecurity Risk Management Strategy for Alert Originators

DTIC Science & Technology

2014-03-01

formerly known as the Commercial Mobile Alert Service ( CMAS ) RDT&E program, is a collaborative partnership that includes the cellular industry, the...Examples illustrate a STRIDE analysis of the generic mission 1 The CMAS Alerting Pipeline Taxonomy describes in detail a hierarchical classification...SEI-2013-SR-018 | 1 1 Introduction The Wireless Emergency Alerts (WEA) service, formerly known as the Commercial Mobile Alert Service ( CMAS ), is a

Dental History Predictors of Caries Related Dental Emergencies.

DTIC Science & Technology

1981-11-01

10+) 50% of those with U- lesions would be selected and only 4% of those without disease would be selected. The accuracy of such a system as well as...sufficient sensitivity, specificity, and diagnostic power to be useful as predictive tools. Dental health classification systems are typically only...predicted with some reliability given the intimacy of the relationship and the relatively long duration of the pre-emergency state. The incidence of

Comparison of diagnostic classification systems for delirium with new research criteria that incorporate the three core domains.

PubMed

Trzepacz, Paula T; Meagher, David J; Franco, José G

2016-05-01

Diagnostic classification systems do not incorporate phenomenological research findings about the three core symptom domains of delirium (Attentional/Cognitive, Circadian, Higher Level Thinking). We evaluated classification performances of novel Trzepacz, Meagher, and Franco research diagnostic criteria (TMF) that incorporate those domains and ICD-10, DSM-III-R, DSM-IV, and DSM-5. Primary data analysis of 641 patients with mixed neuropsychiatric profiles. Delirium (n=429) and nondelirium (n=212) reference standard groups were identified using cluster analysis of symptoms assessed using the Delirium Rating Scale-Revised-98. Accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), and likelihood ratios (LR+, LR-) are reported. TMF criteria had high sensitivity and specificity (87.4% and 89.2%), more balanced than DSM-III-R (100% and 31.6%), DSM-IV (97.7% and 74.1%), DSM-5 (97.7% and 72.6%), and ICD-10 (66.2% and 100%). PPV of DSM-III-R, DSM-IV, and DSM-5 were <90.0%, while PPV for ICD-10 and TMF were >90%. ICD-10 had the lowest NPV (59.4%). TMF had the highest LR+ (8.06) and DSM-III-R the lowest LR- (0.0). Overall, values for DSM-IV and DSM-5 were similar, whereas for ICD-10 and DSM-III-R were inverse of each other. In the pre-existing cognitive impairment/dementia subsample (n=128), TMF retained its highest LR+ though specificity (58.3%) became less well balanced with sensitivity (87.9%), which still exceeded that of DSM. TMF research diagnostic criteria performed well, with more balanced sensitivity and specificity and the highest likelihood ratio for delirium identification. Reflecting the three core domains of delirium, TMF criteria may have advantages in biological research where delineation of this syndrome is important. Copyright © 2016. Published by Elsevier Inc.

The distribution of lung cancer across sectors of society in the United Kingdom: a study using national primary care data.

PubMed

Iyen-Omofoman, Barbara; Hubbard, Richard B; Smith, Chris J P; Sparks, Emily; Bradley, Emma; Bourke, Alison; Tata, Laila J

2011-11-10

There is pressing need to diagnose lung cancer earlier in the United Kingdom (UK) and it is likely that research using computerised general practice records will help this process. Linkage of these records to area-level geo-demographic classifications may also facilitate case ascertainment for public health programmes, however, there have as yet been no extensive studies of data validity for such purposes. To first address the need for validation, we assessed the completeness and representativeness of lung cancer data from The Health Improvement Network (THIN) national primary care database by comparing incidence and survival between 2000 and 2009 with the UK National Cancer Registry and the National Lung Cancer Audit Database. Secondly, we explored the potential of a geo-demographic social marketing tool to facilitate disease ascertainment by using Experian's Mosaic Public Sector ™ classification, to identify detailed profiles of the sectors of society where lung cancer incidence was highest. Overall incidence of lung cancer (41.4/100, 000 person-years, 95% confidence interval 40.6-42.1) and median survival (232 days) were similar to other national data; The incidence rate in THIN from 2003-2006 was found to be just over 93% of the national cancer registry rate. Incidence increased considerably with area-level deprivation measured by the Townsend Index and was highest in the North-West of England (65.1/100, 000 person-years). Wider variations in incidence were however identified using Mosaic classifications with the highest incidence in Mosaic Public Sector ™types 'Cared-for pensioners, ' 'Old people in flats' and 'Dignified dependency' (191.7, 174.2 and 117.1 per 100, 000 person-years respectively). Routine electronic data in THIN are a valid source of lung cancer information. Mosaic ™ identified greater incidence differentials than standard area-level measures and as such could be used as a tool for public health programmes to ascertain future cases more effectively.

Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae.

PubMed

Krajacich, Benjamin J; Meyers, Jacob I; Alout, Haoues; Dabiré, Roch K; Dowell, Floyd E; Foy, Brian D

2017-11-07

Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites. Regression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5-97.0% for grouping of mosquitoes into "early" and "late" age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%. Variation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders.

Compilation and analysis of global surface water concentrations for individual insecticide compounds.

PubMed

Stehle, Sebastian; Bub, Sascha; Schulz, Ralf

2018-10-15

The decades-long agricultural use of insecticides resulted in frequent contamination of surface waters globally regularly posing high risks for the aquatic biodiversity. However, the concentration levels of individual insecticide compounds have by now not been compiled and reported using global scale data, hampering our knowledge on the insecticide exposure of aquatic ecosystems. Here, we specify measured insecticide concentrations (MICs, comprising in total 11,300 water and sediment concentrations taken from a previous publication) for 28 important insecticide compounds covering four major insecticide classes. Results show that organochlorine and organophosphate insecticides, which dominated the global insecticide market for decades, have been detected most often and at highest concentration levels in surface waters globally. In comparison, MICs of the more recent pyrethroids and neonicotinoids were less often reported and generally at lower concentrations as a result of their later market introduction and lower application rates. An online insecticide classification calculator (ICC; available at: https://static.magic.eco/icc/v1) is provided in order to enable the comparison and classification of prospective MICs with available global insecticide concentrations. Spatial analyses of existing data show that most MICs were reported for surface waters in North America, Asia and Europe, whereas highest concentration levels were detected in Africa, Asia and South America. An evaluation of water and sediment MICs showed that theoretical organic carbon-water partition coefficients (K OC ) determined in the laboratory overestimated K OC values based on actual field concentrations by up to a factor of more than 20, with highest deviations found for highly sorptive pyrethroids. Overall, the comprehensive compilation of insecticide field concentrations presented here is a valuable tool for the classification of future surface water monitoring results and serves as important input data for more field relevant toxicity testing approaches and pesticide exposure and risk assessment schemes. Copyright © 2018 Elsevier B.V. All rights reserved.

A Novel Classification System for Injuries After Electronic Cigarette Explosions.

PubMed

Patterson, Scott B; Beckett, Allison R; Lintner, Alicia; Leahey, Carly; Greer, Ashley; Brevard, Sidney B; Simmons, Jon D; Kahn, Steven A

Electronic cigarettes (e-cigarettes) contain lithium batteries that have been known to explode and/or cause fires that have resulted in burn injury. The purpose of this article is to present a case study, review injuries caused by e-cigarettes, and present a novel classification system from the newly emerging patterns of burns. A case study was presented and online media reports for e-cigarette burns were queried with search terms "e-cigarette burns" and "electronic cigarette burns." The reports and injury patterns were tabulated. Analysis was then performed to create a novel classification system based on the distinct injury patterns seen in the study. Two patients were seen at our regional burn center after e-cigarette burns. One had an injury to his thigh and penis that required operative intervention after ignition of this device in his pocket. The second had a facial burn and corneal abrasions when the device exploded while he was inhaling vapor. The Internet search and case studies resulted in 26 cases for evaluation. The burn patterns were divided in direct injury from the device igniting and indirect injury when the device caused a house or car fire. A numerical classification was created: direct injury: type 1 (hand injury) 7 cases, type 2 (face injury) 8 cases, type 3 (waist/groin injury) 11 cases, and type 5a (inhalation injury from using device) 2 cases; indirect injury: type 4 (house fire injury) 7 cases and type 5b (inhalation injury from fire started by the device) 4 cases. Multiple e-cigarette injuries are occurring in the United States and distinct patterns of burns are emerging. The classification system developed in this article will aid in further study and future regulation of these dangerous devices.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm.

PubMed

Hu, Bin; Li, Xiaowei; Sun, Shuting; Ratcliffe, Martyn

2018-01-01

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4.5 algorithm and other classification algorithms. The classification performance was measured 10 times with different 3-fold cross validation data. The data was derived from 10 subjects while they were attempting to learn material in a simulated distance learning environment. A self-assessment model of self-report was used with a single valence to evaluate attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much better performance, giving the highest correct classification rate (CCR) of % for the valence dimension divided into three classes.

Evaluation of space SAR as a land-cover classification

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

1985-01-01

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces

NASA Astrophysics Data System (ADS)

Hwang, Han-Jeong; Lim, Jeong-Hwan; Kim, Do-Won; Im, Chang-Hwan

2014-07-01

A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promising neuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focused on enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated the performances of a variety of mental task combinations in order to determine the mental task pairs that are best suited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses while seven participants performed eight different mental tasks. Classification accuracies were then estimated for all possible pairs of the eight mental tasks (C=28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: "mental multiplication," "mental rotation," and "right-hand motor imagery." Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.

Obsessive compulsive and related disorders: comparing DSM-5 and ICD-11.

PubMed

Marras, Anna; Fineberg, Naomi; Pallanti, Stefano

2016-08-01

Obsessive-compulsive disorder (OCD) has been recognized as mainly characterized by compulsivity rather than anxiety and, therefore, was removed from the anxiety disorders chapter and given its own in both the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the Beta Draft Version of the 11th revision of the World Health Organization (WHO) International Classification of Diseases (ICD-11). This revised clustering is based on increasing evidence of common affected neurocircuits between disorders, differently from previous classification systems based on interrater agreement. In this article, we focus on the classification of obsessive-compulsive and related disorders (OCRDs), examining the differences in approach adopted by these 2 nosological systems, with particular attention to the proposed changes in the forthcoming ICD-11. At this stage, notable differences in the ICD classification are emerging from the previous revision, apparently converging toward a reformulation of OCRDs that is closer to the DSM-5.

Contributions for classification of platelet rich plasma - proposal of a new classification: MARSPILL.

PubMed

Lana, Jose Fabio Santos Duarte; Purita, Joseph; Paulus, Christian; Huber, Stephany Cares; Rodrigues, Bruno Lima; Rodrigues, Ana Amélia; Santana, Maria Helena; Madureira, João Lopo; Malheiros Luzo, Ângela Cristina; Belangero, William Dias; Annichino-Bizzacchi, Joyce Maria

2017-07-01

Platelet-rich plasma (PRP) has emerged as a significant therapy used in medical conditions with heterogeneous results. There are some important classifications to try to standardize the PRP procedure. The aim of this report is to describe PRP contents studying celular and molecular components, and also propose a new classification for PRP. The main focus is on mononuclear cells, which comprise progenitor cells and monocytes. In addition, there are important variables related to PRP application incorporated in this study, which are the harvest method, activation, red blood cells, number of spins, image guidance, leukocytes number and light activation. The other focus is the discussion about progenitor cells presence on peripherial blood which are interesting due to neovasculogenesis and proliferation. The function of monocytes (in tissue-macrophages) are discussed here and also its plasticity, a potential property for regenerative medicine treatments.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

Functional outcomes in children and young people with dyskinetic cerebral palsy.

PubMed

Monbaliu, Elegast; De La Peña, Mary-Grace; Ortibus, Els; Molenaers, Guy; Deklerck, Jan; Feys, Hilde

2017-06-01

This cross-sectional study aimed to map the functional profile of individuals with dyskinetic cerebral palsy (CP), to determine interrelationships between the functional classification systems, and to investigate the relationship of functional abilities with dystonia and choreoathetosis severity. Fifty-five children (<15y) and young people (15-22y) (30 males, 25 females; mean age 14y 6mo, standard deviation 4y 1mo) with dyskinetic CP were assessed using the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS), Eating and Drinking Ability Classification System (EDACS), and Viking Speech Scale (VSS), as well as the Dyskinesia Impairment Scale. Over 50 per cent of the participants exhibited the highest limitation levels in GMFCS, MACS, and VSS. Better functional abilities were seen in EDACS and CFCS. Moderate to excellent interrelationship was found among the classification scales. All scales had significant correlation (r s =0.65 - 0.81) with dystonia severity except for CFCS in the young people group. Finally, only MACS (r s =0.40) and EDACS (r s =0.55) in the young people group demonstrated significant correlation with choreoathetosis severity. The need for inclusion of speech, eating, and drinking in the functional assessment of dyskinetic CP is highlighted. The study further supports the strategy of managing dystonia in particular at a younger age followed by choreoathetosis in a later stage. © 2017 Mac Keith Press.

Successional stage of biological soil crusts: an accurate indicator of ecohydrological condition

USGS Publications Warehouse

Belnap, Jayne; Wilcox, Bradford P.; Van Scoyoc, Matthew V.; Phillips, Susan L.

2013-01-01

Biological soil crusts are a key component of many dryland ecosystems. Following disturbance, biological soil crusts will recover in stages. Recently, a simple classification of these stages has been developed, largely on the basis of external features of the crusts, which reflects their level of development (LOD). The classification system has six LOD classes, from low (1) to high (6). To determine whether the LOD of a crust is related to its ecohydrological function, we used rainfall simulation to evaluate differences in infiltration, runoff, and erosion among crusts in the various LODs, across a range of soil depths and with different wetting pre-treatments. We found large differences between the lowest and highest LODs, with runoff and erosion being greatest from the lowest LOD. Under dry antecedent conditions, about 50% of the water applied ran off the lowest LOD plots, whereas less than 10% ran off the plots of the two highest LODs. Similarly, sediment loss was 400 g m-2 from the lowest LOD and almost zero from the higher LODs. We scaled up the results from these simulations using the Rangeland Hydrology and Erosion Model. Modelling results indicate that erosion increases dramatically as slope length and gradient increase, especially beyond the threshold values of 10 m for slope length and 10% for slope gradient. Our findings confirm that the LOD classification is a quick, easy, nondestructive, and accurate index of hydrological condition and should be incorporated in field and modelling assessments of ecosystem health.

Pediatric falls ages 0-4: understanding demographics, mechanisms, and injury severities.

PubMed

Chaudhary, Sofia; Figueroa, Janet; Shaikh, Salah; Mays, Elizabeth Williams; Bayakly, Rana; Javed, Mahwish; Smith, Matthew Lee; Moran, Tim P; Rupp, Jonathan; Nieb, Sharon

2018-04-10

Pediatric unintentional falls are the leading cause of injury-related emergency visits for children < 5 years old. The purpose of this study was to identify population characteristics, injury mechanisms, and injury severities and patterns among children < 5 years to better inform age-appropriate falls prevention strategies. This retrospective database study used trauma registry data from the lead pediatric trauma system in Georgia. Data were analyzed for all patients < 5 years with an international classification of disease, 9th revision, clinical modification (ICD-9 CM) external cause of injury code (E-code) for unintentional falls between 1/1/2013 and 12/31/2015. Age (months) was compared across categories of demographic variables, injury mechanisms, and emergency department (ED) disposition using Kruskal-Wallis ANOVA and the Mann Whitney U test. The relationships between demographic variables, mechanism of injury (MOI), and Injury Severity Score (ISS) were evaluated using multinomial logistic regression. Inclusion criteria were met by 1086 patients (median age = 28 months; 59.7% male; 53.8% White; 49.1% <  1 m fall height). Younger children, < 1-year-old, primarily fell from caregiver's arms, bed, or furniture, while older children sustained more falls from furniture and playgrounds. Children who fell from playground equipment were older (median = 49 months, p < 0.01) than those who fell from the bed (median = 10 months), stairs (median = 18 months), or furniture (median = 19 months). Children < 1 year had the highest proportion of head injuries including skull fracture (63.1%) and intracranial hemorrhage (65.5%), 2-year-old children had the highest proportion of femur fractures (32.9%), and 4-year-old children had the highest proportion of humerus fractures (41.0%). Medicaid patients were younger (median = 24.5 months, p < 0.01) than private payer (median = 34 months). Black patients were younger (median = 20.5 months, p < 0.001) than White patients (median = 29 months). Results from multinomial logistic regression models suggest that as age increases, odds of a severe ISS (16-25) decreased (OR = 0.95, CI = 0.93-0.97). Pediatric unintentional falls are a significant burden of injury for children < 5 years. Future work will use these risk and injury profiles to inform current safety recommendations and develop evidence-based interventions for parents/caregivers and pediatric providers.

Classification of patients by severity grades during triage in the emergency department using data mining methods.

PubMed

Zmiri, Dror; Shahar, Yuval; Taieb-Maimon, Meirav

2012-04-01

To test the feasibility of classifying emergency department patients into severity grades using data mining methods. Emergency department records of 402 patients were classified into five severity grades by two expert physicians. The Naïve Bayes and C4.5 algorithms were applied to produce classifiers from patient data into severity grades. The classifiers' results over several subsets of the data were compared with the physicians' assessments, with a random classifier, and with a classifier that selects the maximal-prevalence class. Positive predictive value, multiple-class extensions of sensitivity and specificity combinations, and entropy change. The mean accuracy of the data mining classifiers was 52.94 ± 5.89%, significantly better (P < 0.05) than the mean accuracy of a random classifier (34.60 ± 2.40%). The entropy of the input data sets was reduced through classification by a mean of 10.1%. Allowing for classification deviations of one severity grade led to mean accuracy of 85.42 ± 1.42%. The classifiers' accuracy in that case was similar to the physicians' consensus rate. Learning from consensus records led to better performance. Reducing the number of severity grades improved results in certain cases. The performance of the Naïve Bayes and C4.5 algorithms was similar; in unbalanced data sets, Naïve Bayes performed better. It is possible to produce a computerized classification model for the severity grade of triage patients, using data mining methods. Learning from patient records regarding which there is a consensus of several physicians is preferable to learning from each physician's patients. Either Naïve Bayes or C4.5 can be used; Naïve Bayes is preferable for unbalanced data sets. An ambiguity in the intermediate severity grades seems to hamper both the physicians' agreement and the classifiers' accuracy. © 2010 Blackwell Publishing Ltd.

Errors in imaging patients in the emergency setting

PubMed Central

Reginelli, Alfonso; Lo Re, Giuseppe; Midiri, Federico; Muzj, Carlo; Romano, Luigia; Brunese, Luca

2016-01-01

Emergency and trauma care produces a “perfect storm” for radiological errors: uncooperative patients, inadequate histories, time-critical decisions, concurrent tasks and often junior personnel working after hours in busy emergency departments. The main cause of diagnostic errors in the emergency department is the failure to correctly interpret radiographs, and the majority of diagnoses missed on radiographs are fractures. Missed diagnoses potentially have important consequences for patients, clinicians and radiologists. Radiologists play a pivotal role in the diagnostic assessment of polytrauma patients and of patients with non-traumatic craniothoracoabdominal emergencies, and key elements to reduce errors in the emergency setting are knowledge, experience and the correct application of imaging protocols. This article aims to highlight the definition and classification of errors in radiology, the causes of errors in emergency radiology and the spectrum of diagnostic errors in radiography, ultrasonography and CT in the emergency setting. PMID:26838955

Errors in imaging patients in the emergency setting.

PubMed

Pinto, Antonio; Reginelli, Alfonso; Pinto, Fabio; Lo Re, Giuseppe; Midiri, Federico; Muzj, Carlo; Romano, Luigia; Brunese, Luca

2016-01-01

Emergency and trauma care produces a "perfect storm" for radiological errors: uncooperative patients, inadequate histories, time-critical decisions, concurrent tasks and often junior personnel working after hours in busy emergency departments. The main cause of diagnostic errors in the emergency department is the failure to correctly interpret radiographs, and the majority of diagnoses missed on radiographs are fractures. Missed diagnoses potentially have important consequences for patients, clinicians and radiologists. Radiologists play a pivotal role in the diagnostic assessment of polytrauma patients and of patients with non-traumatic craniothoracoabdominal emergencies, and key elements to reduce errors in the emergency setting are knowledge, experience and the correct application of imaging protocols. This article aims to highlight the definition and classification of errors in radiology, the causes of errors in emergency radiology and the spectrum of diagnostic errors in radiography, ultrasonography and CT in the emergency setting.

Classificatory multiplicity: intimate partner violence diagnosis in emergency department consultations.

PubMed

Olive, Philippa

2017-08-01

To explore the naming, or classification, of physical assaults by a partner as 'intimate partner violence' during emergency department consultations. Research continues to evidence instances when intimate partner physical violence is 'missed' or unacknowledged during emergency department consultations. Theoretically, this research was approached through complexity theory and the sociology of diagnosis. Research design was an applied, descriptive and explanatory, multiple-method approach that combined qualitative semistructured interviews with service-users (n = 8) and emergency department practitioners (n = 9), and qualitative and quantitative document analysis of emergency department health records (n = 28). This study found that multiple classifications of intimate partner violence were mobilised during emergency department consultations and that these different versions of intimate partner violence held different diagnostic categories, processes and consequences. The construction of different versions of intimate partner violence in emergency department consultations could explain variance in people's experiences and outcomes of consultations. The research found that the classificatory threshold for 'intimate partner violence' was too high. Strengthening systems of diagnosis (identification and intervention) so that all incidents of partner violence are named as 'intimate partner violence' would reduce the incidence of missed cases and afford earlier specialist intervention to reduce violence and limit its harms. This research found that identification of and response to intimate partner violence, even in contexts of severe physical violence, was contingent. By lowering the classificatory threshold so that all incidents of partner violence are named as 'intimate partner violence', practitioners could make a significant contribution to reducing missed intimate partner violence during consultations and improving health outcomes for this population. This research has relevance for practitioners in any setting where service-user report of intimate partner violence is possible. © 2016 John Wiley & Sons Ltd.

A new diagnosis grouping system for child emergency department visits.

PubMed

Alessandrini, Evaline A; Alpern, Elizabeth R; Chamberlain, James M; Shea, Judy A; Gorelick, Marc H

2010-02-01

A clinically sensible system of grouping diseases is needed for describing pediatric emergency diagnoses for research and reporting. This project aimed to create an International Classification of Diseases (ICD)-based diagnosis grouping system (DGS) for child emergency department (ED) visits that is 1) clinically sensible with regard to how diagnoses are grouped and 2) comprehensive in accounting for nearly all diagnoses (>95%). The second objective was to assess the construct validity of the DGS by examining variation in the frequency of targeted groups of diagnoses within the concepts of season, age, sex, and hospital type. A panel of general and pediatric emergency physicians used the nominal group technique and Delphi surveys to create the DGS. The primary data source used to develop the DGS was the Pediatric Emergency Care Applied Research Network (PECARN) Core Data Project (PCDP). A total of 3,041 ICD-9 codes, accounting for 98.9% of all diagnoses in the PCDP, served as the basis for creation of the DGS. The expert panel developed a DGS framework representing a clinical approach to the diagnosis and treatment of pediatric emergency patients. The resulting DGS has 21 major groups and 77 subgroups and accounts for 96.5% to 99% of diagnoses when applied to three external data sets. Variations in the frequency of targeted groups of diagnoses related to seasonality, age, sex, and site of care confirm construct validity. The DGS offers a clinically sensible method for describing pediatric ED visits by grouping ICD-9 codes in a consensus-derived classification scheme. This system may be used for research, reporting, needs assessment, and resource planning. (c) 2010 by the Society for Academic Emergency Medicine.

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity.

PubMed

Öztoprak, Hüseyin; Toycan, Mehmet; Alp, Yaşar Kemal; Arıkan, Orhan; Doğutepe, Elvin; Karakaş, Sirel

2017-12-01

Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems. A novel classification approach that discriminates ADHD and nonADHD groups over the time-frequency domain features of event-related potential (ERP) recordings that are taken during Stroop task is presented. Time-Frequency Hermite-Atomizer (TFHA) technique is used for the extraction of high resolution time-frequency domain features that are highly localized in time-frequency domain. Based on an extensive investigation, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain the best discriminating features. When the best three features were used, the classification accuracy for the training dataset reached 98%, and the use of five features further improved the accuracy to 99.5%. The accuracy was 100% for the testing dataset. Based on extensive experiments, the delta band emerged as the most contributing frequency band and statistical parameters emerged as the most contributing feature group. The classification performance of this study suggests that TFHA can be employed as an auxiliary component of the diagnostic and prognostic procedures for ADHD. The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

The foodscape: classification and field validation of secondary data sources across urban/rural and socio-economic classifications in England

PubMed Central

2012-01-01

Background In recent years, alongside the exponential increase in the prevalence of overweight and obesity, there has been a change in the food environment (foodscape). This research focuses on methods used to measure and classify the foodscape. This paper describes the foodscape across urban/rural and socio-economic divides. It examines the validity of a database of food outlets obtained from Local Authority sources (secondary level & desk based), across urban/rural and socio-economic divides by conducting fieldwork (ground-truthing). Additionally this paper tests the efficacy of using a desk based classification system to describe food outlets, compared with ground-truthing. Methods Six geographically defined study areas were purposively selected within North East England consisting of two Lower Super Output Areas (LSOAs; a small administrative geography) each. Lists of food outlets were obtained from relevant Local Authorities (secondary level & desk based) and fieldwork (ground-truthing) was conducted. Food outlets were classified using an existing tool. Positive predictive values (PPVs) and sensitivity analysis was conducted to explore validation of secondary data sources. Agreement between 'desk' and 'field' based classifications of food outlets were assessed. Results There were 438 food outlets within all study areas; the urban low socio-economic status (SES) area had the highest number of total outlets (n = 210) and the rural high SES area had the least (n = 19). Differences in the types of outlets across areas were observed. Comparing the Local Authority list to fieldwork across the geographical areas resulted in a range of PPV values obtained; with the highest in urban low SES areas (87%) and the lowest in Rural mixed SES (79%). While sensitivity ranged from 95% in the rural mixed SES area to 60% in the rural low SES area. There were no significant associations between field/desk percentage agreements across any of the divides. Conclusion Despite the relatively small number of areas, this work furthers our understanding of the validity of using secondary data sources to identify and classify the foodscape in a variety of geographical settings. While classification of the foodscape using secondary Local Authority food outlet data with information obtained from the internet, is not without its difficulties, desk based classification would be an acceptable alternative to fieldwork, although it should be used with caution. PMID:22472206

Multispectral LiDAR Data for Land Cover Classification of Urban Areas

PubMed Central

Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

2017-01-01

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. PMID:28445432

Multispectral LiDAR Data for Land Cover Classification of Urban Areas.

PubMed

Morsy, Salem; Shaker, Ahmed; El-Rabbany, Ahmed

2017-04-26

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy.

Detecting asphalt pavement raveling using emerging 3D laser technology and macrotexture analysis.

DOT National Transportation Integrated Search

2015-08-01

This research project comprehensively tested and validated the automatic raveling detection, classification, : and measurement algorithms using 3D laser technology that were developed through a project sponsored by : the National Cooperative Highway ...

Mental Disorder-The Need for an Accurate Definition.

PubMed

Telles-Correia, Diogo; Saraiva, Sérgio; Gonçalves, Jorge

2018-01-01

There are several reasons why a definition for mental disorder is essential. Among these are not only reasons linked to psychiatry itself as a science (nosology, research) but also to ethical, legal, and financial issues. The first formal definition of mental disorder resulted from a deep conceptual analysis led by Robert Spitzer. It emerged to address several challenges that psychiatry faced at the time, namely to serve as the starting point for an atheoretical and evidence-based classification of mental disorders, to justify the removal of homosexuality from classifications, and to counter the arguments of antipsychiatry. This definition has been updated, with some conceptual changes that make it depart from the main assumptions of Spitzer's original definition. In this article, we intend to review the factors that substantiated the emergence of the first formal definition of mental disorder that based all its later versions.

Enhanced risk management by an emerging multi-agent architecture

NASA Astrophysics Data System (ADS)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Pay and Benefits of Leaders at 475 Colleges and Universities: A Survey.

ERIC Educational Resources Information Center

Chronicle of Higher Education, 1999

1999-01-01

Presents data from institutional tax forms for the pay and benefits of key personnel at 475 private colleges and universities for 1996-97 and 1997-98. Institutions are grouped by Carnegie Classification and listings typically include data for six positions, such as the president, highest paid faculty member, a senior vice president, and chief…

40 CFR 131.38 - Establishment of numeric criteria for priority toxic pollutants for the State of California.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Great Lakes Water Quality Initiative Criteria Documents for the Protection of Aquatic Life in Ambient... water quality criteria to protect against acute effects in aquatic life and is the highest instream... any aquatic life or human health use classifications in the Water Quality Control Plans for the...

42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

Code of Federal Regulations, 2010 CFR

2010-10-01

... of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and services within a group are not comparable with respect to the use of resources if the highest median cost... an item or service within the group. (2) CMS may make exceptions to the requirements set forth in...

42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

Code of Federal Regulations, 2011 CFR

2011-10-01

... of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and services within a group are not comparable with respect to the use of resources if the highest median cost... an item or service within the group. (2) CMS may make exceptions to the requirements set forth in...

34 CFR 222.23 - How does a local educational agency determine the aggregate assessed value of its eligible...

Code of Federal Regulations, 2010 CFR

2010-07-01

... adding together the assessed values determined pursuant to paragraph (a)(4) of this section for all... Uses for Determining Base Values Tax classifications of adjacent properties based on highest and best... aggregate assessed value of its eligible Federal property for its section 8002 payment? 222.23 Section 222...

34 CFR 222.23 - How does a local educational agency determine the aggregate assessed value of its eligible...

Code of Federal Regulations, 2012 CFR

2012-07-01

... adding together the assessed values determined pursuant to paragraph (a)(4) of this section for all... Uses for Determining Base Values Tax classifications of adjacent properties based on highest and best... aggregate assessed value of its eligible Federal property for its section 8002 payment? 222.23 Section 222...

34 CFR 222.23 - How does a local educational agency determine the aggregate assessed value of its eligible...

Code of Federal Regulations, 2011 CFR

2011-07-01

... adding together the assessed values determined pursuant to paragraph (a)(4) of this section for all... Uses for Determining Base Values Tax classifications of adjacent properties based on highest and best... aggregate assessed value of its eligible Federal property for its section 8002 payment? 222.23 Section 222...

34 CFR 222.23 - How does a local educational agency determine the aggregate assessed value of its eligible...

Code of Federal Regulations, 2013 CFR

2013-07-01

... adding together the assessed values determined pursuant to paragraph (a)(4) of this section for all... Uses for Determining Base Values Tax classifications of adjacent properties based on highest and best... aggregate assessed value of its eligible Federal property for its section 8002 payment? 222.23 Section 222...

34 CFR 222.23 - How does a local educational agency determine the aggregate assessed value of its eligible...

Code of Federal Regulations, 2014 CFR

2014-07-01

... adding together the assessed values determined pursuant to paragraph (a)(4) of this section for all... Uses for Determining Base Values Tax classifications of adjacent properties based on highest and best... aggregate assessed value of its eligible Federal property for its section 8002 payment? 222.23 Section 222...

Not Your Father's PE: Obesity, Exercise, and the Role of Schools

ERIC Educational Resources Information Center

Cawley, John; Meyerhoefer, Chad; Newhouse, David

2006-01-01

American children are gaining weight at an alarming rate. Since the 1960s, according to the Centers for Disease Control and Prevention (CDC), the percentage of American six- to eleven-year-olds who fall into the CDC's highest weight classification for children has almost quadrupled. Requiring more physical education (PE) seems like a logical…

Betel Nut Chewing Behavior among Adolescents in Eastern Taiwan: A Cluster Analysis

ERIC Educational Resources Information Center

Chen, Han-Ying; Waigandt, Alex C.

2009-01-01

The prevalence of betel nut chewing among junior high school students is highest in the eastern region of Taiwan (Lin, 1990). Although there is some research on the prevalence rate, little effort has been paid to developing a classification of betel nut chewing behavior applicable to adolescents. Eight-hundred and forty-three students, including…

A pilot asthma incidence surveillance system and case definition: lessons learned.

PubMed

Trepka, Mary Jo; Martin, Pilar; Mavunda, Kunjana; Rodriguez, Diana; Zhang, Guoyan; Brown, Clive

2009-01-01

Surveillance for incident asthma in the general population could provide timely information about asthma trends and new, emerging etiologic factors. We sought to determine the feasibility of an asthma incidence surveillance system using voluntary reporting of asthma by outpatient clinics and emergency departments (EDs). Voluntary reporting occurred from July 2002 through June 2006. We classified reported asthma based on a case definition adapted from one developed by the Council of State and Territorial Epidemiologists. We validated the case definition by having pulmonologists review data from participant interviews, medical record abstractions, and pulmonary function test (PFT) results. The positive predictive value (PPV) of meeting any of the case definition criteria for asthma was 80% to 82%. The criterion of taking at least one rescue and one controller medication had the highest PPV (97% to 100%). Only 7% of people meeting the incident case definition had a PFT documented in their medical record, limiting the usefulness of PFT results for case classification. Compared with pediatric participants, adult participants were more likely to be uninsured and to obtain asthma care at EDs. The surveillance system cost $5129 per enrolled person meeting the incident case definition and was difficult to implement in participating clinics and EDs because asthma reporting was not mandatory and informed consent was necessary. The project was useful in evaluating the case definition's validity and in describing the participants' characteristics and health-care use patterns. However, without mandatory reporting laws, reporting of incident asthma in the general population by clinicians is not likely to be a feasible method for asthma surveillance.

Estimating workload using EEG spectral power and ERPs in the n-back task

NASA Astrophysics Data System (ADS)

Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert

2012-08-01

Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.

Genetics-Based Classification of Filoviruses Calls for Expanded Sampling of Genomic Sequences

PubMed Central

Lauber, Chris; Gorbalenya, Alexander E.

2012-01-01

We have recently developed a computational approach for hierarchical, genome-based classification of viruses of a family (DEmARC). In DEmARC, virus clusters are delimited objectively by devising a universal family-wide threshold on intra-cluster genetic divergence of viruses that is specific for each level of the classification. Here, we apply DEmARC to a set of 56 filoviruses with complete genome sequences and compare the resulting classification to the ICTV taxonomy of the family Filoviridae. We find in total six candidate taxon levels two of which correspond to the species and genus ranks of the family. At these two levels, the six filovirus species and two genera officially recognized by ICTV, as well as a seventh tentative species for Lloviu virus and prototyping a third genus, are reproduced. DEmARC lends the highest possible support for these two as well as the four other levels, implying that the actual number of valid taxon levels remains uncertain and the choice of levels for filovirus species and genera is arbitrary. Based on our experience with other virus families, we conclude that the current sampling of filovirus genomic sequences needs to be considerably expanded in order to resolve these uncertainties in the framework of genetics-based classification. PMID:23170166

Genetics-based classification of filoviruses calls for expanded sampling of genomic sequences.

PubMed

Lauber, Chris; Gorbalenya, Alexander E

2012-09-01

We have recently developed a computational approach for hierarchical, genome-based classification of viruses of a family (DEmARC). In DEmARC, virus clusters are delimited objectively by devising a universal family-wide threshold on intra-cluster genetic divergence of viruses that is specific for each level of the classification. Here, we apply DEmARC to a set of 56 filoviruses with complete genome sequences and compare the resulting classification to the ICTV taxonomy of the family Filoviridae. We find in total six candidate taxon levels two of which correspond to the species and genus ranks of the family. At these two levels, the six filovirus species and two genera officially recognized by ICTV, as well as a seventh tentative species for Lloviu virus and prototyping a third genus, are reproduced. DEmARC lends the highest possible support for these two as well as the four other levels, implying that the actual number of valid taxon levels remains uncertain and the choice of levels for filovirus species and genera is arbitrary. Based on our experience with other virus families, we conclude that the current sampling of filovirus genomic sequences needs to be considerably expanded in order to resolve these uncertainties in the framework of genetics-based classification.

Genomic ancestry and ethnoracial self-classification based on 5,871 community-dwelling Brazilians (The Epigen Initiative)

PubMed Central

Lima-Costa, M. Fernanda; Rodrigues, Laura C.; Barreto, Maurício L.; Gouveia, Mateus; Horta, Bernardo L.; Mambrini, Juliana; Kehdy, Fernanda S. G.; Pereira, Alexandre; Rodrigues-Soares, Fernanda; Victora, Cesar G.; Tarazona-Santos, Eduardo; Cesar, Cibele C.; Conceição, Jackson S.; Costa, Gustavo N.O.; Esteban, Nubia; Fiaccone, Rosemeire L.; Figueiredo, Camila A.; Firmo, Josélia O.A.; Horimoto, Andrea R.V.R.; Leal, Thiago P.; Machado, Moara; Magalhães, Wagner C.S.; de Oliveira, Isabel Oliveira; Peixoto, Sérgio V.; Rodrigues, Maíra R.; Santos, Hadassa C.; Silva, Thiago M.

2015-01-01

Brazil never had segregation laws defining membership of an ethnoracial group. Thus, the composition of the Brazilian population is mixed, and its ethnoracial classification is complex. Previous studies showed conflicting results on the correlation between genome ancestry and ethnoracial classification in Brazilians. We used 370,539 Single Nucleotide Polymorphisms to quantify this correlation in 5,851 community-dwelling individuals in the South (Pelotas), Southeast (Bambui) and Northeast (Salvador) Brazil. European ancestry was predominant in Pelotas and Bambui (median = 85.3% and 83.8%, respectively). African ancestry was highest in Salvador (median = 50.5%). The strength of the association between the phenotype and median proportion of African ancestry varied largely across populations, with pseudo R2 values of 0.50 in Pelotas, 0.22 in Bambui and 0.13 in Salvador. The continuous proportion of African genomic ancestry showed a significant S-shape positive association with self-reported Blacks in the three sites, and the reverse trend was found for self reported Whites, with most consistent classifications in the extremes of the high and low proportion of African ancestry. In self-classified Mixed individuals, the predicted probability of having African ancestry was bell-shaped. Our results support the view that ethnoracial self-classification is affected by both genome ancestry and non-biological factors. PMID:25913126

Comparison on three classification techniques for sex estimation from the bone length of Asian children below 19 years old: an analysis using different group of ages.

PubMed

Darmawan, M F; Yusuf, Suhaila M; Kadir, M R Abdul; Haron, H

2015-02-01

Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

Genomic ancestry and ethnoracial self-classification based on 5,871 community-dwelling Brazilians (The Epigen Initiative).

PubMed

Lima-Costa, M Fernanda; Rodrigues, Laura C; Barreto, Maurício L; Gouveia, Mateus; Horta, Bernardo L; Mambrini, Juliana; Kehdy, Fernanda S G; Pereira, Alexandre; Rodrigues-Soares, Fernanda; Victora, Cesar G; Tarazona-Santos, Eduardo

2015-04-27

Brazil never had segregation laws defining membership of an ethnoracial group. Thus, the composition of the Brazilian population is mixed, and its ethnoracial classification is complex. Previous studies showed conflicting results on the correlation between genome ancestry and ethnoracial classification in Brazilians. We used 370,539 Single Nucleotide Polymorphisms to quantify this correlation in 5,851 community-dwelling individuals in the South (Pelotas), Southeast (Bambui) and Northeast (Salvador) Brazil. European ancestry was predominant in Pelotas and Bambui (median = 85.3% and 83.8%, respectively). African ancestry was highest in Salvador (median = 50.5%). The strength of the association between the phenotype and median proportion of African ancestry varied largely across populations, with pseudo R(2) values of 0.50 in Pelotas, 0.22 in Bambui and 0.13 in Salvador. The continuous proportion of African genomic ancestry showed a significant S-shape positive association with self-reported Blacks in the three sites, and the reverse trend was found for self reported Whites, with most consistent classifications in the extremes of the high and low proportion of African ancestry. In self-classified Mixed individuals, the predicted probability of having African ancestry was bell-shaped. Our results support the view that ethnoracial self-classification is affected by both genome ancestry and non-biological factors.

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Bloodstain pattern classification: Accuracy, effect of contextual information and the role of analyst characteristics.

PubMed

Osborne, Nikola K P; Taylor, Michael C; Healey, Matthew; Zajac, Rachel

2016-03-01

It is becoming increasingly apparent that contextual information can exert a considerable influence on decisions about forensic evidence. Here, we explored accuracy and contextual influence in bloodstain pattern classification, and how these variables might relate to analyst characteristics. Thirty-nine bloodstain pattern analysts with varying degrees of experience each completed measures of compliance, decision-making style, and need for closure. Analysts then examined a bloodstain pattern without any additional contextual information, and allocated votes to listed pattern types according to favoured and less favoured classifications. Next, if they believed it would assist with their classification, analysts could request items of contextual information - from commonly encountered sources of information in bloodstain pattern analysis - and update their vote allocation. We calculated a shift score for each item of contextual information based on vote reallocation. Almost all forms of contextual information influenced decision-making, with medical findings leading to the highest shift scores. Although there was a small positive association between shift scores and the degree to which analysts displayed an intuitive decision-making style, shift scores did not vary meaningfully as a function of experience or the other characteristics measured. Almost all of the erroneous classifications were made by novice analysts. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

What clues are available for differential diagnosis of headaches in emergency settings?

PubMed

Mert, Ertan; Ozge, Aynur; Taşdelen, Bahar; Yilmaz, Arda; Bilgin, Nursel G

2008-04-01

The correct diagnosis of headache disorders in an emergency room is important for developing early management strategies and determining optimal emergency room activities. This prospective clinical based study was performed in order to determine demographic and clinical clues for differential diagnosis of primary and secondary headache disorders and also to obtain a classification plot for the emergency room practitioners. This study included 174 patients older than 15 years of age presenting in the emergency room with a chief complaint of headache. Definite headache diagnoses were made according to ICHD-II criteria. Classification and regression tree was used as new method for the statistical analysis of the differential diagnostic process. Our 174 patients with headache were diagnosed as basically primary (72.9%) and secondary (27.1%) headaches. Univariate analysis with cross tabs showed three important results. First, unilateral pain location caused 1.431-fold increase in the primary headache risk (p = 0.006). Second, having any triggers caused 1.440-fold increase in the primary headache risk (p = 0.001). Third, having associated co-morbid medical disorders caused 4.643-fold increase in the secondary headache risk (p < 0.001). It was concluded that the presence of comorbidity, the patient's age, the existence of trigger and relaxing factors, the pain in other body parts that accompanies headache and the quality of pain in terms of location and duration were all important clues for physicians in making an accurate differentiation between primary and secondary headaches.

Identification of Air Force Emerging Technologies and Militarily Significant Emerging Technologies.

DTIC Science & Technology

1985-08-31

taking an integrated approach to avionics and EU, the various sensors and receivers on the aircraft can time-share the use of common signal processors...functions mentioned above has required, in addition to a separate sensor or antenna, a totally independent electronics suite. Many of the advanced...Classification A3. IMAGING SENSOR AUTOPROCESSOR The Air Force has contracted with Rockwell International and Honeywell in this work. Rockwell’s work is

[Classification and monitoring of the appropriateness of emergency admissions in a tertiary hospital].

PubMed

López-Picazo Ferrer, J J; Tomás García, N; Cubillana Herrero, J D; Gómez Company, J A; de Dios Cánovas García, J

2014-01-01

To measure the appropriateness of hospital admissions, to classify its Clinical Services (CS) according to the level of inappropriateness, and to determine the usefulness of applying rapid assessment techniques (lot quality assurance sampling) in these types of measurements. A descriptive, retrospective study was conducted in a tertiary hospital to assess the clinical records of emergency admissions to the 12 CS with a higher volume of admissions, using the Appropriateness Evaluation Protocol (AEP). A four-level («A» to «D») increasingly inadequate admissions scale was constructed setting both standard and threshold values in every stratum. Every CS was classified in one of them using lot quality assurance sampling (LQAS). A total of 156 cases (13 cases from every CS) were assessed. The assessment effort (devoted time) was also estimated. There were 22.4±6.3% of inadequate admissions. In the CS classification, 9 (75%) got a good or acceptable appropriateness level, and only 1 (8%) got an inacceptable level. The time devoted was estimated at 17 hours. AEP is useful to assess the admission appropriateness and may be included in the «Emergencies» process management, although its variability prevents the use for external comparisons. If both LQAS and the appropriateness classification level and the global estimation (by unifying lot samples) are combined, the monitoring is affordable without a great effort. To extend these tools to other quality indicators requiring direct observation or clinical records, manual assessment could improve the monitoring efficiency. Copyright © 2013 SECA. Published by Elsevier Espana. All rights reserved.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

A Bayesian Approach to Genome/Linguistic Relationships in Native South Americans

PubMed Central

Amorim, Carlos Eduardo Guerra; Bisso-Machado, Rafael; Ramallo, Virginia; Bortolini, Maria Cátira; Bonatto, Sandro Luis; Salzano, Francisco Mauro; Hünemeier, Tábita

2013-01-01

The relationship between the evolution of genes and languages has been studied for over three decades. These studies rely on the assumption that languages, as many other cultural traits, evolve in a gene-like manner, accumulating heritable diversity through time and being subjected to evolutionary mechanisms of change. In the present work we used genetic data to evaluate South American linguistic classifications. We compared discordant models of language classifications to the current Native American genome-wide variation using realistic demographic models analyzed under an Approximate Bayesian Computation (ABC) framework. Data on 381 STRs spread along the autosomes were gathered from the literature for populations representing the five main South Amerindian linguistic groups: Andean, Arawakan, Chibchan-Paezan, Macro-Jê, and Tupí. The results indicated a higher posterior probability for the classification proposed by J.H. Greenberg in 1987, although L. Campbell's 1997 classification cannot be ruled out. Based on Greenberg's classification, it was possible to date the time of Tupí-Arawakan divergence (2.8 kya), and the time of emergence of the structure between present day major language groups in South America (3.1 kya). PMID:23696865

A scheme for a flexible classification of dietary and health biomarkers.

PubMed

Gao, Qian; Praticò, Giulia; Scalbert, Augustin; Vergères, Guy; Kolehmainen, Marjukka; Manach, Claudine; Brennan, Lorraine; Afman, Lydia A; Wishart, David S; Andres-Lacueva, Cristina; Garcia-Aloy, Mar; Verhagen, Hans; Feskens, Edith J M; Dragsted, Lars O

2017-01-01

Biomarkers are an efficient means to examine intakes or exposures and their biological effects and to assess system susceptibility. Aided by novel profiling technologies, the biomarker research field is undergoing rapid development and new putative biomarkers are continuously emerging in the scientific literature. However, the existing concepts for classification of biomarkers in the dietary and health area may be ambiguous, leading to uncertainty about their application. In order to better understand the potential of biomarkers and to communicate their use and application, it is imperative to have a solid scheme for biomarker classification that will provide a well-defined ontology for the field. In this manuscript, we provide an improved scheme for biomarker classification based on their intended use rather than the technology or outcomes (six subclasses are suggested: food compound intake biomarkers (FCIBs), food or food component intake biomarkers (FIBs), dietary pattern biomarkers (DPBs), food compound status biomarkers (FCSBs), effect biomarkers, physiological or health state biomarkers). The application of this scheme is described in detail for the dietary and health area and is compared with previous biomarker classification for this field of research.

A bayesian approach to genome/linguistic relationships in native South Americans.

PubMed

Amorim, Carlos Eduardo Guerra; Bisso-Machado, Rafael; Ramallo, Virginia; Bortolini, Maria Cátira; Bonatto, Sandro Luis; Salzano, Francisco Mauro; Hünemeier, Tábita

2013-01-01

The relationship between the evolution of genes and languages has been studied for over three decades. These studies rely on the assumption that languages, as many other cultural traits, evolve in a gene-like manner, accumulating heritable diversity through time and being subjected to evolutionary mechanisms of change. In the present work we used genetic data to evaluate South American linguistic classifications. We compared discordant models of language classifications to the current Native American genome-wide variation using realistic demographic models analyzed under an Approximate Bayesian Computation (ABC) framework. Data on 381 STRs spread along the autosomes were gathered from the literature for populations representing the five main South Amerindian linguistic groups: Andean, Arawakan, Chibchan-Paezan, Macro-Jê, and Tupí. The results indicated a higher posterior probability for the classification proposed by J.H. Greenberg in 1987, although L. Campbell's 1997 classification cannot be ruled out. Based on Greenberg's classification, it was possible to date the time of Tupí-Arawakan divergence (2.8 kya), and the time of emergence of the structure between present day major language groups in South America (3.1 kya).

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

PubMed Central

Abou Zeid, Elias; Rezazadeh Sereshkeh, Alborz; Schultz, Benjamin; Chau, Tom

2017-01-01

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control. Here, we propose a ternary BCI based on single-trial RPs. This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. A pipeline of spatio-temporal filtering with per participant parameter optimization was used for feature extraction. The ternary classification was decomposed into binary classifications using a decision-directed acyclic graph (DDAG). For each class pair in the DDAG structure, an ordered diversified classifier system (ODCS-DDAG) was used to select the best among various classification algorithms or to combine the results of different classification algorithms. Using EEG data from 14 participants performing self-initiated left or right key presses, punctuated with rest periods, we compared the performance of ODCS-DDAG to a ternary classifier and four popular multiclass decomposition methods using only a single classification algorithm. ODCS-DDAG had the highest performance (0.769 Cohen's Kappa score) and was significantly better than the ternary classifier and two of the four multiclass decomposition methods. Our work supports further study of RP-based BCI for intuitive asynchronous environmental control or augmentative communication. PMID:28596725

Innovation and stasis: technology and race in Mark Twain's Pudd'nhead Wilson.

PubMed

Current, Cynthia A

2009-01-01

Mark Twain's Pudd'nhead Wilson demonstrates how technologies of identification attempt to counter how bodies evolve beyond previous constraints—in particular, the constraints of racial classification. Twain develops accounts of subjectivity and racial classification that cover an extraordinary breadth of genealogy, biology, and law, while still invoking elements of randomness and chance. The key to such combinations of fixity and emergence in human identity is the technology of fingerprinting. Twain's speculative engagement with fingerprinting creates a system and medium to classify and secure particular forms of identity, leading to the reassertion of racial values already inherent in science and technology, law, and commerce. Fingerprinting represents the direction that technologies of identity would seek to employ: a movement away from direct visual observation of bodies, whose emergence and change over time make them difficult to categorize, to reliance on archives of information that are increasingly removed from the contexts of meaning and emergence those bodies inhabit; this reflects "one drop" politics, as race becomes increasingly difficult to define visually. The archive itself, then, becomes infected with the spectacular vitality of, and the speculation and risk within, nineteenth-century biological and cultural determinism.

Topographic, bioclimatic, and vegetation characteristics of three ecoregion classification systems in North America: Comparisons along continent-wide transects

USGS Publications Warehouse

Thompson, R.S.; Shafer, S.L.; Anderson, K.H.; Strickland, L.E.; Pelltier, R.T.; Bartlein, P.J.; Kerwin, M.W.

2005-01-01

Ecoregion classification systems are increasingly used for policy and management decisions, particularly among conservation and natural resource managers. A number of ecoregion classification systems are currently available, with each system defining ecoregions using different classification methods and different types of data. As a result, each classification system describes a unique set of ecoregions. To help potential users choose the most appropriate ecoregion system for their particular application, we used three latitudinal transects across North America to compare the boundaries and environmental characteristics of three ecoregion classification systems [Ku??chler, World Wildlife Fund (WWF), and Bailey]. A variety of variables were used to evaluate the three systems, including woody plant species richness, normalized difference in vegetation index (NDVI), and bioclimatic variables (e.g., mean temperature of the coldest month) along each transect. Our results are dominated by geographic patterns in temperature, which are generally aligned north-south, and in moisture, which are generally aligned east-west. In the west, the dramatic changes in physiography, climate, and vegetation impose stronger controls on ecoregion boundaries than in the east. The Ku??chler system has the greatest number of ecoregions on all three transects, but does not necessarily have the highest degree of internal consistency within its ecoregions with regard to the bioclimatic and species richness data. In general, the WWF system appears to track climatic and floristic variables the best of the three systems, but not in all regions on all transects. ?? 2005 Springer Science+Business Media, Inc.

Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry.

PubMed

Katuwal, Gajendra J; Baum, Stefi A; Cahill, Nathan D; Michael, Andrew M

2016-01-01

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7-8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4-5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13-18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD.

Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry

PubMed Central

Baum, Stefi A.; Cahill, Nathan D.; Michael, Andrew M.

2016-01-01

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD. PMID:27065101

Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature

PubMed Central

Holmes, John H; Elliott, Thomas E; Brown, Jeffrey S; Raebel, Marsha A; Davidson, Arthur; Nelson, Andrew F; Chung, Annie; La Chance, Pierre; Steiner, John F

2014-01-01

Objective To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). Materials and methods Medline, PubMed, EMBASE, CINAHL, and INSPEC were searched for relevant documents published through July 31, 2013 using a systematic approach. Only documents relating to DRNs in the USA were included. Documents were analyzed using a classification framework consisting of 10 facets to identify themes. Results 6641 documents were retrieved. After screening for duplicates and relevance, 38 were included in the final review. A peer-reviewed literature on data warehouse governance is emerging, but is still sparse. Peer-reviewed publications on UK research network governance were more prevalent, although not reviewed for this analysis. All 10 classification facets were used, with some documents falling into two or more classifications. No document addressed costs associated with governance. Discussion Even though DRNs are emerging as vehicles for research and public health surveillance, understanding of DRN data governance policies and procedures is limited. This is expected to change as more DRN projects disseminate their governance approaches as publicly available toolkits and peer-reviewed publications. Conclusions While peer-reviewed, US-based DRN data warehouse governance publications have increased, DRN developers and administrators are encouraged to publish information about these programs. PMID:24682495

African Language Resource Handbook: A Resource Handbook of the Eighty-two Highest Priority African Languages. Prepublication Edition.

ERIC Educational Resources Information Center

Dwyer, David J.; Yankee, Everyl

A directory of the 82 African languages given high priority for instruction in the United States contains a profile for each language that includes its classification and where it is spoken, the number of speakers, dialect situation, usage, orthography status, and listings of related human and institutional resources for the purpose of…

42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

Code of Federal Regulations, 2013 CFR

2013-10-01

... set forth in paragraph (a)(1) in unusual cases, such as low volume items and services, but may not... and in terms of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and services within a group are not comparable with respect to the use of resources if the highest...

42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

Code of Federal Regulations, 2012 CFR

2012-10-01

... and in terms of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and services within a group are not comparable with respect to the use of resources if the highest... cost for an item or service within the group. (2) CMS may make exceptions to the requirements set forth...

42 CFR 419.31 - Ambulatory payment classification (APC) system and payment weights.

Code of Federal Regulations, 2014 CFR

2014-10-01

... set forth in paragraph (a)(1) in unusual cases, such as low volume items and services, but may not... and in terms of resource use into APC groups. Except as specified in paragraph (a)(2) of this section, items and services within a group are not comparable with respect to the use of resources if the highest...

An 11-Year Analysis of Black Students' Experience of Problems and Use of Services: Implications for Counseling Professionals.

ERIC Educational Resources Information Center

June, Lee N.; And Others

1990-01-01

Examined problems experienced and services used by Black college students (N=1,261) over 11 years. Found issues of finances, academic adjustment, and living conditions were ranked highest. Use of several services could be predicted by sex, classification, age, and residence, but use of services was not always consistent with rankings, particularly…

Effect of Aptitude on the Performance of Army Communications Operators

DTIC Science & Technology

1992-01-01

equipment is usually concentrated at the highest echelons of command. 9 SLOWER 1 RT-773GRC-103(V) DA-437 GRC- 103M ~ (SYSTEM 1) ANGRC.153(V (SYSTEM 2) OA...Sg EU. ’-44 rh Ca- REFERENCES Campbell, John P. (ed.), "Project A: The U.S. Army Selection and Classification Project," Personnel Psychology, Vol. 43(2

Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2014-03-01

As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.

Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Lu, Guolan; Halig, Luma; Wang, Dongsheng; Qin, Xulei; Chen, Zhuo Georgia; Fei, Baowei

2014-10-01

Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

Upper Gastrointestinal Hemorrhage: Development of the Severity Score.

PubMed

Chaikitamnuaychok, Rangson; Patumanond, Jayanton

2012-12-01

Emergency endoscopy for every patient with upper gastrointestinal hemorrhage is not possible in many medical centers. Simple guidelines to select patients for emergency endoscopy are lacking. The aim of the present report is to develop a simple scoring system to classify upper gastrointestinal hemorrhage (UGIH) severity based on patient clinical profiles at the emergency departments. Retrospective data of patients with UGIH in a university affiliated hospital were analyzed. Patients were criterion-classified into 3 severity levels: mild, moderate and severe. Clinical and laboratory information were compared among the 3 groups. Significant parameters were selected as indicators of severity. Coefficients of significant multivariable parameters were transformed into item scores, which added up as individual severity scores. The scores were used to classify patients into 3 urgency levels: non-urgent, urgent and emergent groups. Score-classification and criterion-classification were compared. Significant parameters in the model were age ≥ 60 years, pulse rate ≥ 100/min, systolic blood pressure < 100 mmHg, hemoglobin < 10 g/dL, blood urea nitrogen ≥ 35 mg/dL, presence of cirrhosis and hepatic failure. The score ranged from 0 to 27, and classifying patients into 3 urgency groups: non-urgent (score < 4, n = 215, 21.2%), urgent (score 4 - 16, n = 677, 66.9%) and emergent (score > 16, n = 121, 11.9%). The score correctly classified 81.4% of the patients into their original (criterion-classified) severity groups. Under-estimation (7.5%) and over-estimation (11.1%) were clinically acceptable. Our UGIH severity scoring system classified patients into 3 urgency groups: non-urgent, urgent and emergent, with clinically acceptable small number of under- and over-estimations. Its discriminative ability and precision should be validated before adopting into clinical practice.

A statistical approach to root system classification

PubMed Central

Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter

2013-01-01

Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for “plant functional type” identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential. PMID:23914200

A statistical approach to root system classification.

PubMed

Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter

2013-01-01

Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for "plant functional type" identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential.

Classification schemes for knowledge translation interventions: a practical resource for researchers.

PubMed

Slaughter, Susan E; Zimmermann, Gabrielle L; Nuspl, Megan; Hanson, Heather M; Albrecht, Lauren; Esmail, Rosmin; Sauro, Khara; Newton, Amanda S; Donald, Maoliosa; Dyson, Michele P; Thomson, Denise; Hartling, Lisa

2017-12-06

As implementation science advances, the number of interventions to promote the translation of evidence into healthcare, health systems, or health policy is growing. Accordingly, classification schemes for these knowledge translation (KT) interventions have emerged. A recent scoping review identified 51 classification schemes of KT interventions to integrate evidence into healthcare practice; however, the review did not evaluate the quality of the classification schemes or provide detailed information to assist researchers in selecting a scheme for their context and purpose. This study aimed to further examine and assess the quality of these classification schemes of KT interventions, and provide information to aid researchers when selecting a classification scheme. We abstracted the following information from each of the original 51 classification scheme articles: authors' objectives; purpose of the scheme and field of application; socioecologic level (individual, organizational, community, system); adaptability (broad versus specific); target group (patients, providers, policy-makers), intent (policy, education, practice), and purpose (dissemination versus implementation). Two reviewers independently evaluated the methodological quality of the development of each classification scheme using an adapted version of the AGREE II tool. Based on these assessments, two independent reviewers reached consensus about whether to recommend each scheme for researcher use, or not. Of the 51 original classification schemes, we excluded seven that were not specific classification schemes, not accessible or duplicates. Of the remaining 44 classification schemes, nine were not recommended. Of the 35 recommended classification schemes, ten focused on behaviour change and six focused on population health. Many schemes (n = 29) addressed practice considerations. Fewer schemes addressed educational or policy objectives. Twenty-five classification schemes had broad applicability, six were specific, and four had elements of both. Twenty-three schemes targeted health providers, nine targeted both patients and providers and one targeted policy-makers. Most classification schemes were intended for implementation rather than dissemination. Thirty-five classification schemes of KT interventions were developed and reported with sufficient rigour to be recommended for use by researchers interested in KT in healthcare. Our additional categorization and quality analysis will aid in selecting suitable classification schemes for research initiatives in the field of implementation science.

14 CFR 23.807 - Emergency exits.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Emergency exits must not be located with respect to any propeller disk or any other potential hazard so as... airplanes certificated for spinning, allow each occupant to abandon the airplane at the highest speed likely to be achieved in the maneuver for which the airplane is certificated. (c) Tests. The proper...

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Classification models for identification of at-risk groups for incident memory complaints.

PubMed

van den Kommer, Tessa N; Comijs, Hannie C; Rijs, Kelly J; Heymans, Martijn W; van Boxtel, Martin P J; Deeg, Dorly J H

2014-02-01

Memory complaints in older adults may be a precursor of measurable cognitive decline. Causes for these complaints may vary across age groups. The goal of this study was to develop classification models for the early identification of persons at risk for memory complaints using a broad range of characteristics. Two age groups were studied, 55-65 years old (N = 1,416.8) and 65-75 years old (N = 471) using data from the Longitudinal Aging Study Amsterdam. Participants reporting memory complaints at baseline were excluded. Data on predictors of memory complaints were collected at baseline and analyzed using logistic regression analyses. Multiple imputation was applied to handle the missing data; missing data due to mortality were not imputed. In persons aged 55-65 years, 14.4% reported memory complaints after three years of follow-up. Persons using medication, who were former smokers and had insufficient/poor hearing, were at the highest risk of developing memory complaints, i.e., a predictive value of 33.3%. In persons 65-75 years old, the incidence of memory complaints was 22.5%. Persons with a low sense of mastery, who reported having pain, were at the highest risk of memory complaints resulting in a final predictive value of 56.9%. In the subsample of persons without a low sense of mastery who (almost) never visited organizations and had a low level of memory performance, 46.8% reported memory complaints at follow-up. The classification models led to the identification of specific target groups at risk for memory complaints. Suggestions for person-tailored interventions may be based on these risk profiles.

[Tobacco quality analysis of industrial classification of different years using near-infrared (NIR) spectrum].

PubMed

Wang, Yi; Xiang, Ma; Wen, Ya-Dong; Yu, Chun-Xia; Wang, Luo-Ping; Zhao, Long-Lian; Li, Jun-Hui

2012-11-01

In this study, tobacco quality analysis of main Industrial classification of different years was carried out applying spectrum projection and correlation methods. The group of data was near-infrared (NIR) spectrum from Hongta Tobacco (Group) Co., Ltd. 5730 tobacco leaf Industrial classification samples from Yuxi in Yunnan province from 2007 to 2010 year were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of HONGDA. The conclusion showed that, when the samples were divided to two part by the ratio of 2:1 randomly as analysis and verification sets in the same year, the verification set corresponded with the analysis set applying spectrum projection because their correlation coefficients were above 0.98. The correlation coefficients between two different years applying spectrum projection were above 0.97. The highest correlation coefficient was the one between 2008 and 2009 year and the lowest correlation coefficient was the one between 2007 and 2010 year. At the same time, The study discussed a method to get the quantitative similarity values of different industrial classification samples. The similarity and consistency values were instructive in combination and replacement of tobacco leaf blending.

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

PubMed Central

Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

2017-01-01

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

[Analysis of vegetation spatial and temporal variations in Qinghai Province based on remote sensing].

PubMed

Wang, Li-wen; Wei, Ya-xing; Niu, Zheng

2008-06-01

1 km MODIS NDVI time series data combining with decision tree classification, supervised classification and unsupervised classification was used to classify land cover type of Qinghai Province into 14 classes. In our classification system, sparse grassland and sparse shrub were emphasized, and their spatial distribution locations were labeled. From digital elevation model (DEM) of Qinghai Province, five elevation belts were achieved, and we utilized geographic information system (GIS) software to analyze vegetation cover variation on different elevation belts. Our research result shows that vegetation cover in Qinghai Province has been improved in recent five years. Vegetation cover area increases from 370047 km2 in 2001 to 374576 km2 in 2006, and vegetation cover rate increases by 0.63%. Among five grade elevation belts, vegetation cover ratio of high mountain belt is the highest (67.92%). The area of middle density grassland in high mountain belt is the largest, of which area is 94 003 km2. Increased area of dense grassland in high mountain belt is the greatest (1280 km2). During five years, the biggest variation is the conversion from sparse grassland to middle density grassland in high mountain belt, of which area is 15931 km2.

Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

PubMed Central

2014-01-01

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms. PMID:24795876

Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon

USGS Publications Warehouse

Davis, Philip A.; Staid, Matthew I.; Plescia, Jeffrey B.; Johnson, Jeffrey R.

2002-01-01

This study examined various types of remote-sensing data that have been acquired during a 12-month period over a portion of the Colorado River corridor to determine the type of data and conditions for data acquisition that provide the optimum classification results for mapping riparian vegetation. Issues related to vegetation mapping included time of year, number and positions of wavelength bands, and spatial resolution for data acquisition to produce accurate vegetation maps versus cost of data. Image data considered in the study consisted of scanned color-infrared (CIR) film, digital CIR, and digital multispectral data, whose resolutions from 11 cm (photographic film) to 100 cm (multispectral), that were acquired during the Spring, Summer, and Fall seasons in 2000 for five long-term monitoring sites containing riparian vegetation. Results show that digitally acquired data produce higher and more consistent classification accuracies for mapping vegetation units than do film products. The highest accuracies were obtained from nine-band multispectral data; however, a four-band subset of these data, that did not include short-wave infrared bands, produced comparable mapping results. The four-band subset consisted of the wavelength bands 0.52-0.59 µm, 0.59-0.62 µm, 0.67-0.72 µm, and 0.73-0.85 µm. Use of only three of these bands that simulate digital CIR sensors produced accuracies for several vegetation units that were 10% lower than those obtained using the full multispectral data set. Classification tests using band ratios produced lower accuracies than those using band reflectance for scanned film data; a result attributed to the relatively poor radiometric fidelity maintained by the film scanning process, whereas calibrated multispectral data produced similar classification accuracies using band reflectance and band ratios. This suggests that the intrinsic band reflectance of the vegetation is more important than inter-band reflectance differences in attaining high mapping accuracies. These results also indicate that radiometrically calibrated sensors that record a wide range of radiance produce superior results and that such sensors should be used for monitoring purposes. When texture (spatial variance) at near-infrared wavelength is combined with spectral data in classification, accuracy increased most markedly (20-30%) for the highest resolution (11-cm) CIR film data, but decreased in its effect on accuracy in lower-resolution multi-spectral image data; a result observed in previous studies (Franklin and McDermid 1993, Franklin et al. 2000, 2001). While many classification unit accuracies obtained from the 11-cm film CIR band with texture data were in fact higher than those produced using the 100-cm, nine-band multispectral data with texture, the 11-cm film CIR data produced much lower accuracies than the 100-cm multispectral data for the more sparsely populated vegetation units due to saturation of picture elements during the film scanning process in vegetation units with a high proportion of alluvium. Overall classification accuracies obtained from spectral band and texture data range from 36% to 78% for all databases considered, from 57% to 71% for the 11-cm film CIR data, and from 54% to 78% for the 100-cm multispectral data. Classification results obtained from 20-cm film CIR band and texture data, which were produced by applying a Gaussian filter to the 11-cm film CIR data, showed increases in accuracy due to texture that were similar to those observed using the original 11-cm film CIR data. This suggests that data can be collected at the lower resolution and still retain the added power of vegetation texture. Classification accuracies for the riparian vegetation units examined in this study do not appear to be influenced by season of data acquisition, although data acquired under direct sunlight produced higher overall accuracies than data acquired under overcast conditions. The latter observation, in addition to the importance of band reflectance for classification, implies that data should be acquired near summer solstice when sun elevation and reflectance is highest and when shadows cast by steep canyon walls are minimized.

Antecedents and precipitants of patient-related violence in the emergency department: Results from the Australian VENT Study (Violence in Emergency Nursing and Triage).

PubMed

Pich, Jacqueline V; Kable, Ashley; Hazelton, Mike

2017-08-01

Workplace violence is one of the most significant and hazardous issues faced by nurses globally. It is a potentially life-threatening and life-affecting workplace hazard often downplayed as just "part of the job" for nurses. A cross-sectional design was used and data were collected using a purpose developed survey tool. Surveys were distributed to all members of the College of Emergency Nurses' Australasia (CENA) in 2010 and 537 eligible responses were received (RR=51%). Patient-related violence was reported by 87% of nurses in the last six months. Precipitants and antecedents for episodes of violence were reported in three categories: nurse-related; patient-related and emergency-department specific factors. Triaging was identified as the highest risk nursing activity, and the triage area identified as the highest risk location in the department. Patients who presented with alcohol intoxication, substance misuse or mental health issues were identified as the groups at greatest risk for potential violence. Patient-related violence was reported by the majority of emergency nurses surveyed. A number of precipitants and antecedents perceived to be risk factors by participants were found to be significant and are unavoidable in the working lives of emergency department nurses. Copyright © 2017 College of Emergency Nursing Australasia. Published by Elsevier Ltd. All rights reserved.

Estimation of Lithological Classification in Taipei Basin: A Bayesian Maximum Entropy Method

NASA Astrophysics Data System (ADS)

Wu, Meng-Ting; Lin, Yuan-Chien; Yu, Hwa-Lung

2015-04-01

In environmental or other scientific applications, we must have a certain understanding of geological lithological composition. Because of restrictions of real conditions, only limited amount of data can be acquired. To find out the lithological distribution in the study area, many spatial statistical methods used to estimate the lithological composition on unsampled points or grids. This study applied the Bayesian Maximum Entropy (BME method), which is an emerging method of the geological spatiotemporal statistics field. The BME method can identify the spatiotemporal correlation of the data, and combine not only the hard data but the soft data to improve estimation. The data of lithological classification is discrete categorical data. Therefore, this research applied Categorical BME to establish a complete three-dimensional Lithological estimation model. Apply the limited hard data from the cores and the soft data generated from the geological dating data and the virtual wells to estimate the three-dimensional lithological classification in Taipei Basin. Keywords: Categorical Bayesian Maximum Entropy method, Lithological Classification, Hydrogeological Setting

The Iterated Classification Game: A New Model of the Cultural Transmission of Language

PubMed Central

Swarup, Samarth; Gasser, Les

2010-01-01

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. PMID:20190877

Welcoming with risk classification in teaching hospitals: assessment of structure, process and result.

PubMed

Vituri, Dagmar Willamowius; Inoue, Kelly Cristina; Bellucci Júnior, José Aparecido; de Oliveira, Carlos Aparecido; Rossi, Robson Marcelo; Matsuda, Laura Misue

2013-01-01

To assess, from the worker's viewpoint, the structure, the process and the results of the Emergency Hospital Services that have taken up the guideline of "Welcoming with Risk Classification" in two teaching hospitals of the state of Paraná. Quantitative and descriptive research, exploratory and prospective, using random sampling stratified by professional category, comprising a universe of 216 professional people. They found some points of agreement regarding the promotion of a welcoming and humane environment; privacy and security; welcome and shelter of the companion and also the sheltering and classification of all patients; however, there was disagreement about the comfort of the environment, reference system and counter-reference, prioritisation of seriously ill patients in post-classification service, communication between the members of the multi-professional team and reassessment of the guideline. The workers assess the development of the guideline as being precarious, due mainly to the lack of physical structure, due to the lack of physical structure and shortcomings in the service process.

A proposed classification scheme for Ada-based software products

NASA Technical Reports Server (NTRS)

Cernosek, Gary J.

1986-01-01

As the requirements for producing software in the Ada language become a reality for projects such as the Space Station, a great amount of Ada-based program code will begin to emerge. Recognizing the potential for varying levels of quality to result in Ada programs, what is needed is a classification scheme that describes the quality of a software product whose source code exists in Ada form. A 5-level classification scheme is proposed that attempts to decompose this potentially broad spectrum of quality which Ada programs may possess. The number of classes and their corresponding names are not as important as the mere fact that there needs to be some set of criteria from which to evaluate programs existing in Ada. An exact criteria for each class is not presented, nor are any detailed suggestions of how to effectively implement this quality assessment. The idea of Ada-based software classification is introduced and a set of requirements from which to base further research and development is suggested.

Classification of polycystic ovary based on ultrasound images using competitive neural network

NASA Astrophysics Data System (ADS)

Dewi, R. M.; Adiwijaya; Wisesty, U. N.; Jondri

2018-03-01

Infertility in the women reproduction system due to inhibition of follicles maturation process causing the number of follicles which is called polycystic ovaries (PCO). PCO detection is still operated manually by a gynecologist by counting the number and size of follicles in the ovaries, so it takes a long time and needs high accuracy. In general, PCO can be detected by calculating stereology or feature extraction and classification. In this paper, we designed a system to classify PCO by using the feature extraction (Gabor Wavelet method) and Competitive Neural Network (CNN). CNN was selected because this method is the combination between Hemming Net and The Max Net so that the data classification can be performed based on the specific characteristics of ultrasound data. Based on the result of system testing, Competitive Neural Network obtained the highest accuracy is 80.84% and the time process is 60.64 seconds (when using 32 feature vectors as well as weight and bias values respectively of 0.03 and 0.002).

Optimizing classification performance in an object-based very-high-resolution land use-land cover urban application

NASA Astrophysics Data System (ADS)

Georganos, Stefanos; Grippa, Tais; Vanhuysse, Sabine; Lennert, Moritz; Shimoni, Michal; Wolff, Eléonore

2017-10-01

This study evaluates the impact of three Feature Selection (FS) algorithms in an Object Based Image Analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation Based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although a significant increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

A classification of marked hijaiyah letters' pronunciation using hidden Markov model

NASA Astrophysics Data System (ADS)

Wisesty, Untari N.; Mubarok, M. Syahrul; Adiwijaya

2017-08-01

Hijaiyah letters are the letters that arrange the words in Al Qur'an consisting of 28 letters. They symbolize the consonant sounds. On the other hand, the vowel sounds are symbolized by harokat/marks. Speech recognition system is a system used to process the sound signal to be data so that it can be recognized by computer. To build the system, some stages are needed i.e characteristics/feature extraction and classification. In this research, LPC and MFCC extraction method, K-Means Quantization vector and Hidden Markov Model classification are used. The data used are the 28 letters and 6 harakat with the total class of 168. After several are testing done, it can be concluded that the system can recognize the pronunciation pattern of marked hijaiyah letter very well in the training data with its highest accuracy of 96.1% using the feature of LPC extraction and 94% using the MFCC. Meanwhile, when testing system is used, the accuracy decreases up to 41%.

The development of a classification schema for arts-based approaches to knowledge translation.

PubMed

Archibald, Mandy M; Caine, Vera; Scott, Shannon D

2014-10-01

Arts-based approaches to knowledge translation are emerging as powerful interprofessional strategies with potential to facilitate evidence uptake, communication, knowledge, attitude, and behavior change across healthcare provider and consumer groups. These strategies are in the early stages of development. To date, no classification system for arts-based knowledge translation exists, which limits development and understandings of effectiveness in evidence syntheses. We developed a classification schema of arts-based knowledge translation strategies based on two mechanisms by which these approaches function: (a) the degree of precision in key message delivery, and (b) the degree of end-user participation. We demonstrate how this classification is necessary to explore how context, time, and location shape arts-based knowledge translation strategies. Classifying arts-based knowledge translation strategies according to their core attributes extends understandings of the appropriateness of these approaches for various healthcare settings and provider groups. The classification schema developed may enhance understanding of how, where, and for whom arts-based knowledge translation approaches are effective, and enable theorizing of essential knowledge translation constructs, such as the influence of context, time, and location on utilization strategies. The classification schema developed may encourage systematic inquiry into the effectiveness of these approaches in diverse interprofessional contexts. © 2014 Sigma Theta Tau International.

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.

Utilization of an Academic Nursing Center.

ERIC Educational Resources Information Center

Cole, Frank L.; Mackey, Thomas

1996-01-01

Using data from an academic nursing center that cared for 3,263 patients over eight months, diseases were classified using International Classification of Diseases codes, and procedures were classified using Current Procedural Terminology codes. Patterns of health care emerged, with implications for clinical teaching. (SK)

Emergency Contraception: Do Your Patients Have a Plan B?

PubMed

Bullock, Holly; Salcedo, Jennifer

2015-12-01

Emergency contraception is used after unprotected sex, inadequately protected sex, or sexual assault to reduce the risk of pregnancy. Of emergency contraceptive methods available in the United States, the copper intrauterine device has the highest efficacy, followed by ulipristal acetate, levonorgestrel-containing emergency contraceptive pills, and the Yuzpe method. However, access to the most effective methods is limited. Although advanced prescription of emergency contraceptive pills and counseling on emergency contraception to all reproductive-aged women is recommended, women should be advised to contact their health care providers after taking emergency contraceptive pills to discuss possible copper intrauterine device placement and other follow-up. Copyright © 2015 Elsevier Inc. All rights reserved.

The Comprehensive AOCMF Classification: Skull Base and Cranial Vault Fractures – Level 2 and 3 Tutorial

PubMed Central

Ieva, Antonio Di; Audigé, Laurent; Kellman, Robert M.; Shumrick, Kevin A.; Ringl, Helmut; Prein, Joachim; Matula, Christian

2014-01-01

The AOCMF Classification Group developed a hierarchical three-level craniomaxillofacial classification system with increasing level of complexity and details. The highest level 1 system distinguish four major anatomical units, including the mandible (code 91), midface (code 92), skull base (code 93), and cranial vault (code 94). This tutorial presents the level 2 and more detailed level 3 systems for the skull base and cranial vault units. The level 2 system describes fracture location outlining the topographic boundaries of the anatomic regions, considering in particular the endocranial and exocranial skull base surfaces. The endocranial skull base is divided into nine regions; a central skull base adjoining a left and right side are divided into the anterior, middle, and posterior skull base. The exocranial skull base surface and cranial vault are divided in regions defined by the names of the bones involved: frontal, parietal, temporal, sphenoid, and occipital bones. The level 3 system allows assessing fracture morphology described by the presence of fracture fragmentation, displacement, and bone loss. A documentation of associated intracranial diagnostic features is proposed. This tutorial is organized in a sequence of sections dealing with the description of the classification system with illustrations of the topographical skull base and cranial vault regions along with rules for fracture location and coding, a series of case examples with clinical imaging and a general discussion on the design of this classification. PMID:25489394

A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.

PubMed

Sahan, Seral; Polat, Kemal; Kodaz, Halife; Güneş, Salih

2007-03-01

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

Estimates of emergency operating capacity in US manufacturing and nonmanufacturing industries

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

Belzer, D.B.; Serot, D.E.; Kellogg, M.A.

1991-03-01

Development of integrated mobilization preparedness policies requires planning estimates of available productive capacity during national emergency conditions. Such estimates must be developed in a manner that allows evaluation of current trends in capacity and the consideration of uncertainties in various data inputs and in engineering assumptions. This study, conducted by Pacific Northwest Laboratory (PNL), developed estimates of emergency operating capacity (EOC) for 446 manufacturing industries at the 4-digit Standard Industrial Classification (SIC) level of aggregation and for 24 key non-manufacturing sectors. This volume presents tabular and graphical results of the historical analysis and projections for each SIC industry. (JF)

A machine learning approach to multi-level ECG signal quality classification.

PubMed

Li, Qiao; Rajagopalan, Cadathur; Clifford, Gari D

2014-12-01

Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07±0.32% and OAc of 99.34±0.07% were gained on the validation fold of MITDB. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Identifying Preschool Children At-Risk of Later Reading Difficulties: Evaluation of two Emergent Literacy Screening Tools

PubMed Central

Wilson, Shauna B.; Lonigan, Christopher J.

2012-01-01

Emergent literacy skills are predictive of children’s early reading success, and literacy achievement in early schooling declines more rapidly for children who are below-average readers. It is therefore important for teachers to identify accurately children at risk for later reading difficulty so children can be exposed to good emergent literacy interventions. In this study, 176 preschoolers were administered two screening tools, the Revised Get Ready to Read! (GRTR-R) and the Individual Growth and Development Indicators (IGDIs), and a diagnostic measure at two time points. Receiver operating characteristic curve analyses revealed that at optimal cut scores, GRTR-R provided more accurate classification of children’s overall emergent literacy skills than did IGDIs. However, neither measure was particularly good at classifying specific emergent literacy skills. PMID:19822699

[Designer drugs in Finland].

PubMed

Tacke, Ulrich; den Hollander, Bjørnar; Simojoki, Kaarlo; Korpi, Esa R; Pihlainen, Katja; Alho, Hannu

2011-01-01

Designer drugs are synthetic psychotropic drugs which are marketed as "legal drugs". Their emergence, rapid spreading and unpredictable effects have challenged the health and substance abuse care. The slow process of classification of an abusable drug has provided too many possibilities for spreading the designer drugs. Once a certain substance receives an illegal drugs classification, dealers and users usually move to another, slightly different molecule that is still legal. In Finland, the Narcotics Act has been amended to the effect that the addition of a new substance to the illegal drug list does not require an amendment to the law.

47 CFR Appendix B to Part 64 - Priority Access Service (PAS) for National Security and Emergency Preparedness (NSEP)

Code of Federal Regulations, 2010 CFR

2010-10-01

... leadership; (iii) Disaster shelter coordination and management; and (iv) Critical Disaster Field Office... telecommunications management and response functions during emergency/disaster situations. 3. Initiate PAS requests... priorities, priority one being the highest. The five priority levels are: 1. Executive Leadership and Policy...

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; de Lautour, Oliver R.

2010-04-01

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Emerging Technologies, ISD, and Learning Environments: Critical Perspectives.

ERIC Educational Resources Information Center

Hannafin, Michael J.

1992-01-01

Reviews the evolution of instructional systems design and computer-based learning environments, focusing on the effects of technological advances. Classification of learning environments is discussed in the context of the dimensions of scope (macrolevel or microlevel), user activity (generative or mathemagenic), educational activity (goal-directed…

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.

Support-vector-based emergent self-organising approach for emotional understanding

NASA Astrophysics Data System (ADS)

Nguwi, Yok-Yen; Cho, Siu-Yeung

2010-12-01

This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

PubMed

Lee, Christine K; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime

2018-04-17

The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

Variable Classifications of Glycemic Index Determined by Glucose Meters

PubMed Central

Lin, Meng-Hsueh Amanda; Wu, Ming-Chang; Lin, Jenshinn

2010-01-01

The study evaluated and compared the differences of glucose responses, incremental area under curve (IAUC), glycemic index (GI) and the classification of GI values between measured by biochemical analyzer (Fuji automatic biochemistry analyzer (FAA)) and three glucose meters: Accue Chek Advantage (AGM), BREEZE 2 (BGM), and Optimum Xceed (OGM). Ten healthy subjects were recruited for the study. The results showed OGM yield highest postprandial glucose responses of 119.6 ± 1.5, followed by FAA, 118.4 ± 1.2, BGM, 117.4 ± 1.4 and AGM, 112.6 ± 1.3 mg/dl respectively. FAA reached highest mean IAUC of 4156 ± 208 mg × min/dl, followed by OGM (3835 ± 270 mg × min/dl), BGM (3730 ± 241 mg × min/dl) and AGM (3394 ± 253 mg × min/dl). Among four methods, OGM produced highest mean GI value than FAA (87 ± 5) than FAA, followed by BGM and AGM (77 ± 1, 68 ± 4 and 63 ± 5, p<0.05). The results suggested that the AGM, BGM and OGM are more variable methods to determine IAUC, GI and rank GI value of food than FAA. The present result does not necessarily apply to other glucose meters. The performance of glucose meter to determine GI value of food should be evaluated and calibrated before use. PMID:20664730

Classification bias in commercial business lists for retail food stores in the U.S.

PubMed

Han, Euna; Powell, Lisa M; Zenk, Shannon N; Rimkus, Leah; Ohri-Vachaspati, Punam; Chaloupka, Frank J

2012-04-18

Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

Classification bias in commercial business lists for retail food stores in the U.S.

PubMed Central

2012-01-01

Background Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. Methods We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. Results D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Conclusion Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition. PMID:22512874

Single-trial EEG RSVP classification using convolutional neural networks

NASA Astrophysics Data System (ADS)

Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

2016-05-01

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks.

PubMed

Totah, Deema; Ojeda, Lauro; Johnson, Daniel D; Gates, Deanna; Mower Provost, Emily; Barton, Kira

2018-01-01

Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task. Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset. Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69-92%. These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications. Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.

Optimizing study design for interobserver reliability: IUGA-ICS classification of complications of prostheses and graft insertion.

PubMed

Haylen, Bernard T; Lee, Joseph; Maher, Chris; Deprest, Jan; Freeman, Robert

2014-06-01

Results of interobserver reliability studies for the International Urogynecological Association-International Continence Society (IUGA-ICS) Complication Classification coding can be greatly influenced by study design factors such as participant instruction, motivation, and test-question clarity. We attempted to optimize these factors. After a 15-min instructional lecture with eight clinical case examples (including images) and with classification/coding charts available, those clinicians attending an IUGA Surgical Complications workshop were presented with eight similar-style test cases over 10 min and asked to code them using the Category, Time and Site classification. Answers were compared to predetermined correct codes obtained by five instigators of the IUGA-ICS prostheses and grafts complications classification. Prelecture and postquiz participant confidence levels using a five-step Likert scale were assessed. Complete sets of answers to the questions (24 codings) were provided by 34 respondents, only three of whom reported prior use of the charts. Average score [n (%)] out of eight, as well as median score (range) for each coding category were: (i) Category: 7.3 (91 %); 7 (4-8); (ii) Time: 7.8 (98 %); 7 (6-8); (iii) Site: 7.2 (90 %); 7 (5-8). Overall, the equivalent calculations (out of 24) were 22.3 (93 %) and 22 (18-24). Mean prelecture confidence was 1.37 (out of 5), rising to 3.85 postquiz. Urogynecologists had the highest correlation with correct coding, followed closely by fellows and general gynecologists. Optimizing training and study design can lead to excellent results for interobserver reliability of the IUGA-ICS Complication Classification coding, with increased participant confidence in complication-coding ability.

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

PubMed

Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

2017-02-01

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

Cognition, culture and utility: plant classification by Paraguayan immigrant farmers in Misiones, Argentina.

PubMed

Kujawska, Monika; Jiménez-Escobar, N David; Nolan, Justin M; Arias-Mutis, Daniel

2017-07-25

This study was conducted in three rural communities of small farmers of Paraguayan origin living in the province of Misiones, Argentina. These Criollos (Mestizos) hail chiefly from departments located in the east of Paraguay, where the climate and flora have similar characteristics as those in Misiones. These ecological features contribute to the continuation and maintenance of knowledge and practices related to the use of plants. Fieldwork was conducted between September 2014 and August 2015. Forty five informants from three rural localities situated along the Parana River participated in an ethno-classification task. For the classification event, photographs of 30 medicinal and edible plants were chosen, specifically those yielding the highest frequency of mention among the members of that community (based on data obtained in the first stage of research in 2014). Variation in local plant classifications was examined and compared using principal component analysis and cluster analysis. We found that people classify plants according to application or use (primarily medicinal, to a lesser extent as edible). Morphology is rarely taken into account, even for very similar and closely-related species such as varieties of palms. In light of our findings, we highlight a dominant functionality model at work in the process of plant cognition and classification among farmers of Paraguayan origin. Salient cultural beliefs and practices associated with rural Paraguayan plant-based medicine are described. Additionally, the manner by which residents' concepts of plants articulate with local folk epistemology is discussed. Culturally constructed use patterns ultimately override morphological variables in rural Paraguayans' ethnobotanical classification.

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

Influence of Urbanicity and County Characteristics on the ...

EPA Pesticide Factsheets

Background: Air pollution epidemiology studies, often conducted in large metropolitan areas due to proximity to regulatory monitors, are limited in their ability to examine potential associations between air pollution exposures and health effects in rural locations. Methods: In a time-stratified case-crossover framework, we examined associations between asthma emergency department (ED) visits in North Carolina (2006-2008) collected by a surveillance system, and short-term ozone exposures using predicted concentrations from the Community Multiscale Air Quality (CMAQ) model. Associations were estimated by county groupings based on four urbanicity classifications (representative of county size and urban proximity) and county health. Results: Ozone was associated with asthma ED visits in all-year and warm season (April-October) analyses [Odds Ratio (OR) =1.019; 95% CI: 0.998, 1.040; OR=1.020; 95% CI: 0.997, 1.044, respectively, for a 20 ppb increase in lag 0-2 days ozone]. The association was strongest in Less Urbanized counties, with no evidence of a positive association in Rural counties. Associations were similar when adjusted for fine particulate matter in copolluant models. Associations were stronger for children (5-17 years of age) compared with other age groups, and for individuals living in counties with poorer health status compared with counties that had the highest health rankings, although estimated associations for these subgroups were imprecise. Conclu

Airway remodelling in the transplanted lung.

PubMed

Kuehnel, Mark; Maegel, Lavinia; Vogel-Claussen, Jens; Robertus, Jan Lukas; Jonigk, Danny

2017-03-01

Following lung transplantation, fibrotic remodelling of the small airways has been recognized for almost 5 decades as the main correlate of chronic graft failure and a major obstacle to long-term survival. Mainly due to airway fibrosis, pulmonary allografts currently show the highest attrition rate of all solid organ transplants, with a 5-year survival rate of 58 % on a worldwide scale. The observation that these morphological changes are not just the hallmark of chronic rejection but rather represent a manifestation of a multitude of alloimmune-dependent and -independent injuries was made more recently, as was the discovery that chronic lung allograft dysfunction manifests in different clinical phenotypes of respiratory impairment and corresponding morphological subentities. Although recent years have seen considerable advances in identifying and categorizing these subgroups on the basis of clinical, functional and histomorphological changes, as well as susceptibility to medicinal treatment, this process is far from over. Since the actual pathophysiological mechanisms governing airway remodelling are still only poorly understood, diagnosis and therapy of chronic lung allograft dysfunction presents a major challenge to clinicians, radiologists and pathologists alike. Here, we review and discuss the current state of the literature on chronic lung allograft dysfunction and shed light on classification systems, corresponding clinical and morphological changes, key cellular players and underlying molecular pathways, as well as on emerging diagnostic and therapeutic approaches.

Intracranial hypertension: classification and patterns of evolution

PubMed Central

Iencean, SM

2008-01-01

Intracranial hypertension (ICH) was systematized in four categories according to its aetiology and pathogenic mechanisms: parenchymatous ICH with an intrinsic cerebral cause; vascular ICH, which has its aetiology in disorders of cerebral blood circulation; ICH caused by disorders of cerebro–spinal fluid dynamics and idiopathic ICH. The increase of intracranial pressure is the first to happen and then intracranial hypertension develops from this initial effect becoming symptomatic; it then acquires its individuality, surpassing the initial disease. The intracranial hypertension syndrome corresponds to the stage at which the increased intracranial pressure can be compensated and the acute form of intracranial hypertension is equivalent to a decompensated ICH syndrome. The decompensation of intracranial hypertension is a condition of instability and appears when the normal intrinsic ratio of intracranial pressure – time fluctuation is changed. The essential conditions for decompensation of intracranial hypertension are: the speed of intracranial pressure increase over normal values, the highest value of abnormal intracranial pressure and the duration of high ICP values. Medical objectives are preventing ICP from exceeding 20 mm Hg and maintaining a normal cerebral blood flow. The emergency therapy is the same for the acute form but each of the four forms of ICH has a specific therapy, according to the pathogenic mechanism and if possible to aetiology. PMID:20108456

Some Experience Using SEN2COR

NASA Astrophysics Data System (ADS)

Pflug, Bringfried; Bieniarz, Jakub; Debaecker, Vincent; Louis, Jérôme; Müller-Wilms, Uwe

2016-04-01

ESA has developed and launched the Sentinel-2A optical imaging mission that delivers optical data products designed to feed downstream services mainly related to land monitoring, emergency management and security. Many of these applications require accurate correction of satellite images for atmospheric effects to ensure the highest quality of scientific exploitation of Sentinel-2 data. Therefore the atmospheric correction processor Sen2Cor was developed by TPZ V on behalf of ESA. TPZ F and DLR have teamed up in order to provide the calibration and validation of the Level-2A processor Sen2Cor. Level-2A processing is applied to Top-Of-Atmosphere (TOA) Level-1C ortho-image reflectance products. Level-2A main output is the Bottom-Of-Atmosphere (BOA) corrected reflectance product. Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification (SC) map with Quality Indicators for cloud and snow probabilities. The poster will present some processing examples of Sen2Cor applied to Sentinel-2A data together with first performance investigations. Different situations will be covered like processing with and without DEM (Digital Elevation Model). Sen2Cor processing is controlled by several configuration parameters. Some examples will be presented demonstrating the influence of different settings of some parameters.

Perampanel for tonic-clonic seizures in idiopathic generalized epilepsy

PubMed Central

Krauss, Gregory L.; Wechsler, Robert T.; Wang, Xue-Feng; DiVentura, Bree; Brandt, Christian; Trinka, Eugen; O'Brien, Terence J.; Laurenza, Antonio; Patten, Anna; Bibbiani, Francesco

2015-01-01

Objective: To assess efficacy and safety of adjunctive perampanel in patients with drug-resistant, primary generalized tonic-clonic (PGTC) seizures in idiopathic generalized epilepsy (IGE). Methods: In this multicenter, double-blind study (ClinicalTrials.gov identifier: NCT01393743; funded by Eisai Inc.), patients 12 years or older with PGTC seizures and IGE were randomized to placebo or perampanel during a 4-week titration period (perampanel uptitrated from 2 to 8 mg/d, or highest tolerated dose) and 13-week maintenance period. The primary endpoint was percent change in PGTC seizure frequency per 28 days (titration plus maintenance vs baseline). The key secondary endpoint (primary endpoint for European Union registration) was 50% PGTC seizure responder rate (patients achieving ≥50% reduction in PGTC seizure frequency; maintenance vs baseline). Treatment-emergent adverse events were monitored. Results: Of 164 randomized patients, 162 comprised the full analysis set (placebo, 81; perampanel, 81). Compared with placebo, perampanel conferred a greater median percent change in PGTC seizure frequency per 28 days (−38.4% vs −76.5%; p < 0.0001) and greater 50% PGTC seizure responder rate (39.5% vs 64.2%; p = 0.0019). During maintenance, 12.3% of placebo-treated patients and 30.9% of perampanel-treated patients achieved PGTC seizure freedom. For the safety analysis (placebo, 82; perampanel, 81), the most frequent treatment-emergent adverse events with perampanel were dizziness (32.1%) and fatigue (14.8%). Conclusions: Adjunctive perampanel was well tolerated and improved control of drug-resistant PGTC seizures in patients with IGE. Classification of evidence: This study provides Class I evidence that adjunctive perampanel reduces PGTC seizure frequency, compared with placebo, in patients with drug-resistant PGTC seizures in IGE. PMID:26296511

Evolutionary characterization of the emerging porcine epidemic diarrhea virus worldwide and 2014 epidemic in Taiwan.

PubMed

Sung, Ming-Hua; Deng, Ming-Chung; Chung, Yi-Hsuan; Huang, Yu-Liang; Chang, Chia-Yi; Lan, Yu-Ching; Chou, Hsin-Lin; Chao, Day-Yu

2015-12-01

Since 2010, a new variant of PEDV belonging to Genogroup 2 has been transmitting in China and further spreading to the Unites States and other Asian countries including Taiwan. In order to characterize in detail the temporal and geographic relationships among PEDV strains, the present study systematically evaluated the evolutionary patterns and phylogenetic resolution in each gene of the whole PEDV genome in order to determine which regions provided the maximal interpretative power. The result was further applied to identify the origin of PEDV that caused the 2014 epidemic in Taiwan. Thirty-four full genome sequences were downloaded from GenBank and divided into three non-mutually exclusive groups, namely, worldwide, Genogroup 2 and China, to cover different ranges of secular and spatial trends. Each dataset was then divided into different alignments by different genes for likelihood mapping and phylogenetic analysis. Our study suggested that both nsp3 and S genes contained the highest phylogenetic signal with substitution rate and phylogenetic topology similar to those obtained from the complete genome. Furthermore, the proportion of nodes with high posterior support (posterior probability >0.8) was similar between nsp3 and S genes. The nsp3 gene sequences from three clinical samples of swine with PEDV infections were aligned with other strains available from GenBank and the results suggested that the virus responsible for the 2014 PEDV outbreak in Taiwan clustered together with Clade I from the US within Genogroup 2. In conclusion, the current study identified the nsp3 gene as an alternative marker for a rapid and unequivocal classification of the circulating PEDV strains which provides complementary information to the S gene in identifying the emergence of epidemic strain resulting from recombination. Copyright © 2015 Elsevier B.V. All rights reserved.

Emergency department discharge prescription errors in an academic medical center

PubMed Central

Belanger, April; Devine, Lauren T.; Lane, Aaron; Condren, Michelle E.

2017-01-01

This study described discharge prescription medication errors written for emergency department patients. This study used content analysis in a cross-sectional design to systematically categorize prescription errors found in a report of 1000 discharge prescriptions submitted in the electronic medical record in February 2015. Two pharmacy team members reviewed the discharge prescription list for errors. Open-ended data were coded by an additional rater for agreement on coding categories. Coding was based upon majority rule. Descriptive statistics were used to address the study objective. Categories evaluated were patient age, provider type, drug class, and type and time of error. The discharge prescription error rate out of 1000 prescriptions was 13.4%, with “incomplete or inadequate prescription” being the most commonly detected error (58.2%). The adult and pediatric error rates were 11.7% and 22.7%, respectively. The antibiotics reviewed had the highest number of errors. The highest within-class error rates were with antianginal medications, antiparasitic medications, antacids, appetite stimulants, and probiotics. Emergency medicine residents wrote the highest percentage of prescriptions (46.7%) and had an error rate of 9.2%. Residents of other specialties wrote 340 prescriptions and had an error rate of 20.9%. Errors occurred most often between 10:00 am and 6:00 pm. PMID:28405061

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

NASA Astrophysics Data System (ADS)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.

Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

NASA Astrophysics Data System (ADS)

Ceballos, G. A.; Hernández, L. F.

2015-04-01

Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

PubMed Central

Parsons, Helen M; Ludwig, Christian; Günther, Ulrich L; Viant, Mark R

2007-01-01

Background Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1H, projections of 2D 1H, 1H J-resolved (pJRES), and intact 2D J-resolved (JRES). Results Here, the effects of the glog transform are compared against two commonly used variance stabilising techniques, autoscaling and Pareto scaling, as well as unscaled data. The four methods are evaluated in terms of the effects on the variance of NMR metabolomics data and on the classification accuracy following multivariate analysis, the latter achieved using principal component analysis followed by linear discriminant analysis. For two of three datasets analysed, classification accuracies were highest following glog transformation: 100% accuracy for discriminating 1D NMR spectra of hypoxic and normoxic invertebrate muscle, and 100% accuracy for discriminating 2D JRES spectra of fish livers sampled from two rivers. For the third dataset, pJRES spectra of urine from two breeds of dog, the glog transform and autoscaling achieved equal highest accuracies. Additionally we extended the glog algorithm to effectively suppress noise, which proved critical for the analysis of 2D JRES spectra. Conclusion We have demonstrated that the glog and extended glog transforms stabilise the technical variance in NMR metabolomics datasets. This significantly improves the discrimination between sample classes and has resulted in higher classification accuracies compared to unscaled, autoscaled or Pareto scaled data. Additionally we have confirmed the broad applicability of the glog approach using three disparate datasets from different biological samples using 1D NMR spectra, 1D projections of 2D JRES spectra, and intact 2D JRES spectra. PMID:17605789

Patterns of Negotiation

NASA Astrophysics Data System (ADS)

Sood, Suresh; Pattinson, Hugh

Traditionally, face-to-face negotiations in the real world have not been looked at as a complex systems interaction of actors resulting in a dynamic and potentially emergent system. If indeed negotiations are an outcome of a dynamic interaction of simpler behavior just as with a complex system, we should be able to see the patterns contributing to the complexities of a negotiation under study. This paper and the supporting research sets out to show B2B (business-to-business) negotiations as complex systems of interacting actors exhibiting dynamic and emergent behavior. This paper discusses the exploratory research based on negotiation simulations in which a large number of business students participate as buyers and sellers. The student interactions are captured on video and a purpose built research method attempts to look for patterns of interactions between actors using visualization techniques traditionally reserved to observe the algorithmic complexity of complex systems. Students are videoed negotiating with partners. Each video is tagged according to a recognized classification and coding scheme for negotiations. The classification relates to the phases through which any particular negotiation might pass, such as laughter, aggression, compromise, and so forth — through some 30 possible categories. Were negotiations more or less successful if they progressed through the categories in different ways? Furthermore, does the data depict emergent pathway segments considered to be more or less successful? This focus on emergence within the data provides further strong support for face-to-face (F2F) negotiations to be construed as complex systems.

77 FR 4248 - Cyazofamid; Pesticide Tolerances for Emergency Exemptions

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-27

.../water/index.htm . Based on the Pesticide Root Zone Model/Exposure Analysis Modeling System (PRZM/EXAMS... Classification System (NAICS) codes have been provided to assist you and others in determining whether this... reliable information.'' This includes exposure through drinking water and in residential settings, but does...

Seizures in Infants and Young Children.

ERIC Educational Resources Information Center

McBrien, Dianne M.; Bonthius, Daniel J.

2000-01-01

This article reviews the most frequent causes of seizure disorders in young children and the classification of different seizure types. It discusses current therapies, including alternatives to medication. Emergency response to seizures is covered a well as non-epileptic episodes that may resemble seizures. Epilepsy's potential impact on the…

Epidemic classification of phytosanitary situations on cereal crops using mathematical modeling

USDA-ARS?s Scientific Manuscript database

Most plant protection researchers and experts divide emerging phytosanitary situations into three classes: epidemic, moderate development of disease, and yield depression. The known principles and methods for estimating these situations (Van der Plank J.E., Kranz J. et al.) do not fully describe th...

The effect of different foot and hand set-up positions on backstroke start performance.

PubMed

de Jesus, Karla; de Jesus, Kelly; Abraldes, J Arturo; Mourão, Luis; Borgonovo-Santos, Márcio; Medeiros, Alexandre I A; Gonçalves, Pedro; Chainok, Phornpot; Fernandes, Ricardo J; Vaz, Mário A P; Vilas-Boas, João Paulo

2016-11-01

Foot and hand set-up position effects were analysed on backstroke start performance. Ten swimmers randomly completed 27 starts grouped in trials (n = 3) of each variation, changing foot (totally immersed, partially and totally emerged) and hand (lowest, highest horizontal and vertical) positioning. Fifteen cameras recorded kinematics, and four force plates collected hands and feet kinetics. Standardised mean difference and 95% confidence intervals were used. Variations with feet immersed have shown lower vertical centre of mass (CM) set-up position (0.16 m), vertical impulse exerted at the hands, horizontal and vertical impulse exerted at the feet (0.28, 0.41, 0.16 N/BW.s, respectively) than feet emerged with hands horizontal and vertically positioned. Most variations with feet partially emerged exhibited higher and lesser vertical impulse exerted at hands than feet immersed and emerged (e.g. vertical handgrip, 0.13, 0.15 N/BW.s, respectively). Variation with feet emerged and hands on the lowest horizontal handgrip depicted shorter horizontal (0.23, 0.26 m) and vertical CM positioning at flight (0.16, 0.15 m) than the highest horizontal and vertical handgrip, respectively. Start variations have not affected 15-m time. Variations with feet partially or totally emerged depicted advantages, but focusing on the entry and underwater biomechanics is relevant for a shorter start time.

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

PubMed

Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

2017-01-01

Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

Using FDA reports to inform a classification for health information technology safety problems

PubMed Central

Ong, Mei-Sing; Runciman, William; Coiera, Enrico

2011-01-01

Objective To expand an emerging classification for problems with health information technology (HIT) using reports submitted to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. Design HIT events submitted to MAUDE were retrieved using a standardized search strategy. Using an emerging classification with 32 categories of HIT problems, a subset of relevant events were iteratively analyzed to identify new categories. Two coders then independently classified the remaining events into one or more categories. Free-text descriptions were analyzed to identify the consequences of events. Measurements Descriptive statistics by number of reported problems per category and by consequence; inter-rater reliability analysis using the κ statistic for the major categories and consequences. Results A search of 899 768 reports from January 2008 to July 2010 yielded 1100 reports about HIT. After removing duplicate and unrelated reports, 678 reports describing 436 events remained. The authors identified four new categories to describe problems with software functionality, system configuration, interface with devices, and network configuration; the authors' classification with 32 categories of HIT problems was expanded by the addition of these four categories. Examination of the 436 events revealed 712 problems, 96% were machine-related, and 4% were problems at the human–computer interface. Almost half (46%) of the events related to hazardous circumstances. Of the 46 events (11%) associated with patient harm, four deaths were linked to HIT problems (0.9% of 436 events). Conclusions Only 0.1% of the MAUDE reports searched were related to HIT. Nevertheless, Food and Drug Administration reports did prove to be a useful new source of information about the nature of software problems and their safety implications with potential to inform strategies for safe design and implementation. PMID:21903979

City housing atmospheric pollutant impact on emergency visit for asthma: A classification and regression tree approach.

PubMed

Mazenq, Julie; Dubus, Jean-Christophe; Gaudart, Jean; Charpin, Denis; Viudes, Gilles; Noel, Guilhem

2017-11-01

Particulate matter, nitrogen dioxide (NO 2 ) and ozone are recognized as the three pollutants that most significantly affect human health. Asthma is a multifactorial disease. However, the place of residence has rarely been investigated. We compared the impact of air pollution, measured near patients' homes, on emergency department (ED) visits for asthma or trauma (controls) within the Provence-Alpes-Côte-d'Azur region. Variables were selected using classification and regression trees on asthmatic and control population, 3-99 years, visiting ED from January 1 to December 31, 2013. Then in a nested case control study, randomization was based on the day of ED visit and on defined age groups. Pollution, meteorological, pollens and viral data measured that day were linked to the patient's ZIP code. A total of 794,884 visits were reported including 6250 for asthma and 278,192 for trauma. Factors associated with an excess risk of emergency visit for asthma included short-term exposure to NO 2 , female gender, high viral load and a combination of low temperature and high humidity. Short-term exposures to high NO 2 concentrations, as assessed close to the homes of the patients, were significantly associated with asthma-related ED visits in children and adults. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying and classifying water hyacinth (Eichhornia crassipes) using the HyMap sensor

NASA Astrophysics Data System (ADS)

Rajapakse, Sepalika S.; Khanna, Shruti; Andrew, Margaret E.; Ustin, Susan L.; Lay, Mui

2006-08-01

In recent years, the impact of aquatic invasive species on biodiversity has become a major global concern. In the Sacramento-San Joaquin Delta region in the Central Valley of California, USA, dense infestations of the invasive aquatic emergent weed, water hyacinth (Eichhornia crassipes) interfere with ecosystem functioning. This silent invader constantly encroaches into waterways, eventually making them unusable by people and uninhabitable to aquatic fauna. Quantifying and mapping invasive plant species in aquatic ecosystems is important for efficient management and implementation of mitigation measures. This paper evaluates the ability of hyperspectral imagery, acquired using the HyMap sensor, for mapping water hyacinth in the Sacramento-San Joaquin Delta region. Classification was performed on sixty-four flightlines acquired over the study site using a decision tree which incorporated Spectral Angle Mapper (SAM) algorithm, absorption feature parameters in the spectral region between 0.4 and 2.5μm, and spectral endmembers. The total image dataset was 130GB. Spectral signatures of other emergent aquatic species like pennywort (Hydrocotyle ranunculoides) and water primrose (Ludwigia peploides) showed close similarity with the water hyacinth spectrum, however, the decision tree successfully discriminated water hyacinth from other emergent aquatic vegetation species. The classification algorithm showed high accuracy (κ value = 0.8) in discriminating water hyacinth.

Intra- and interobserver agreement in the classification and treatment of distal third clavicle fractures.

PubMed

Bishop, Julie Y; Jones, Grant L; Lewis, Brian; Pedroza, Angela

2015-04-01

In treatment of distal third clavicle fractures, the Neer classification system, based on the location of the fracture in relation to the coracoclavicular ligaments, has traditionally been used to determine fracture pattern stability. To determine the intra- and interobserver reliability in the classification of distal third clavicle fractures via standard plain radiographs and the intra- and interobserver agreement in the preferred treatment of these fractures. Cohort study (Diagnosis); Level of evidence, 3. Thirty radiographs of distal clavicle fractures were randomly selected from patients treated for distal clavicle fractures between 2006 and 2011. The radiographs were distributed to 22 shoulder/sports medicine fellowship-trained orthopaedic surgeons. Fourteen surgeons responded and took part in the study. The evaluators were asked to measure the size of the distal fragment, classify the fracture pattern as stable or unstable, assign the Neer classification, and recommend operative versus nonoperative treatment. The radiographs were reordered and redistributed 3 months later. Inter- and intrarater agreement was determined for the distal fragment size, stability of the fracture, Neer classification, and decision to operate. Single variable logistic regression was performed to determine what factors could most accurately predict the decision for surgery. Interrater agreement was fair for distal fragment size, moderate for stability, fair for Neer classification, slight for type IIB and III fractures, and moderate for treatment approach. Intrarater agreement was moderate for distal fragment size categories (κ = 0.50, P < .001) and Neer classification (κ = 0.42, P < .001) and substantial for stable fracture (κ = 0.65, P < .001) and decision to operate (κ = 0.65, P < .001). Fracture stability was the best predictor of treatment, with 89% accuracy (P < .001). Fracture stability determination and the decision to operate had the highest interobserver agreement. Fracture stability was the key determinant of treatment, rather than the Neer classification system or the size of the distal fragment. © 2015 The Author(s).

Post-operative rotator cuff integrity, based on Sugaya's classification, can reflect abduction muscle strength of the shoulder.

PubMed

Yoshida, Masahito; Collin, Phillipe; Josseaume, Thierry; Lädermann, Alexandre; Goto, Hideyuki; Sugimoto, Katumasa; Otsuka, Takanobu

2018-01-01

Magnetic resonance (MR) imaging is common in structural and qualitative assessment of the rotator cuff post-operatively. Rotator cuff integrity has been thought to be associated with clinical outcome. The purpose of this study was to evaluate the inter-observer reliability of cuff integrity (Sugaya's classification) and assess the correlation between Sugaya's classification and the clinical outcome. It was hypothesized that Sugaya's classification would show good reliability and good correlation with the clinical outcome. Post-operative MR images were taken two years post-operatively, following arthroscopic rotator cuff repair. For assessment of inter-rater reliability, all radiographic evaluations for the supraspinatus muscle were done by two orthopaedic surgeons and one radiologist. Rotator cuff integrity was classified into five categories, according to Sugaya's classification. Fatty infiltration was graded into four categories, based on the Fuchs' classification grading system. Muscle hypotrophy was graded as four grades, according to the scale proposed by Warner. The clinical outcome was assessed according to the constant scoring system pre-operatively and 2 years post-operatively. Of the sixty-two consecutive patients with full-thickness rotator cuff tears, fifty-two patients were reviewed in this study. These subjects included twenty-three men and twenty-nine women, with an average age of fifty-seven years. In terms of the inter-rater reliability between orthopaedic surgeons, Sugaya's classification showed the highest agreement [ICC (2.1) = 0.82] for rotator cuff integrity. The grade of fatty infiltration and muscle atrophy demonstrated good agreement, respectively (0.722 and 0.758). With regard to the inter-rater reliability between orthopaedic surgeon and radiologist, Sugaya's classification showed good reliability [ICC (2.1) = 0.70]. On the other hand, fatty infiltration and muscle hypotrophy classifications demonstrated fair and moderate agreement [ICC (2.1) = 0.39 and 0.49]. Although no significant correlation was found between overall post-operative constant score and Sugaya's classification, Sugaya's classification indicated significant correlation with the muscle strength score. Sugaya's classification showed repeatability and good agreement between the orthopaedist and radiologist, who are involved in the patient care for the rotator cuff tear. Common classification of rotator cuff integrity with good reliability will give appropriate information for clinicians to improve the patient care of the rotator cuff tear. This classification also would be helpful to predict the strength of arm abduction in the scapular plane. IV.

Sensitivity and specificity of the gain short-screener for predicting substance use disorders in a large national sample of emerging adults.

PubMed

Smith, Douglas C; Bennett, Kyle M; Dennis, Michael L; Funk, Rodney R

2017-05-01

Emerging Adults (ages 18-25) have the highest prevalence of substance use disorders and rarely receive treatment from the specialty care system. Thus, it is important to have screening instruments specifically developed for emerging adults for use in Screening, Brief Intervention and Referral to Treatment (SBIRT) models. Optimal cutoffs for the widely-used GAIN Short-Screener's (GAIN-SS) Substance Disorder Screener (SDScrY) are not established specifically for emerging adults. Therefore, this study examined the sensitivity and specificity of the SDScrY in predicting emerging adult (ages 18-25) substance use disorders. We analyzed data from emerging adults in a large clinical sample (n=9,808) who completed both the five-item SDScrY (α=0.85) and the full criteria set for DSM-IV Substance Use Disorders. We estimated the sensitivity, specificity and area under the curve to determine optimal cutoffs. Analyses revealed a high correlation between the SDScrY screener and its longer parent scale (r=0.95, p<0.001). Sensitivity (83%) and specificity (95%) were highest at a cutoff score of two (AUC=94%) on the SDScrY for any past year substance use disorder. Sensitivity (85%) was also high at a cutoff score of two on the SDScrY for any past year alcohol disorder. The five-item Substance Use Disorder Screener is a sensitive and specific screener for emerging adults, and could be used to identify emerging adults who may benefit from SBIRT interventions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Examining the Potential for Response to Intervention (RTI) Delivery Models in Secondary Education: Emerging Research and Opportunities

ERIC Educational Resources Information Center

Epler, Pam

2017-01-01

To provide the highest quality of education to students, school administrators must adopt new frameworks to meet learners' needs. This allows teaching practices to be optimized to create a meaningful learning environment. "Examining the Potential for Response to Intervention (RTI) Delivery Models in Secondary Education: Emerging Research and…

Social Vulnerability and Ebola Virus Disease in Rural Liberia.

PubMed

Stanturf, John A; Goodrick, Scott L; Warren, Melvin L; Charnley, Susan; Stegall, Christie M

2015-01-01

The Ebola virus disease (EVD) epidemic that has stricken thousands of people in the three West African countries of Liberia, Sierra Leone, and Guinea highlights the lack of adaptive capacity in post-conflict countries. The scarcity of health services in particular renders these populations vulnerable to multiple interacting stressors including food insecurity, climate change, and the cascading effects of disease epidemics such as EVD. However, the spatial distribution of vulnerable rural populations and the individual stressors contributing to their vulnerability are unknown. We developed a Social Vulnerability Classification using census indicators and mapped it at the district scale for Liberia. According to the Classification, we estimate that districts having the highest social vulnerability lie in the north and west of Liberia in Lofa, Bong, Grand Cape Mount, and Bomi Counties. Three of these counties together with the capital Monrovia and surrounding Montserrado and Margibi counties experienced the highest levels of EVD infections in Liberia. Vulnerability has multiple dimensions and a classification developed from multiple variables provides a more holistic view of vulnerability than single indicators such as food insecurity or scarcity of health care facilities. Few rural Liberians are food secure and many cannot reach a medical clinic in <80 minutes. Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations. Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted. We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

PubMed

Bouts, Mark J R J; Möller, Christiane; Hafkemeijer, Anne; van Swieten, John C; Dopper, Elise; van der Flier, Wiesje M; Vrenken, Hugo; Wink, Alle Meije; Pijnenburg, Yolande A L; Scheltens, Philip; Barkhof, Frederik; Schouten, Tijn M; de Vos, Frank; Feis, Rogier A; van der Grond, Jeroen; de Rooij, Mark; Rombouts, Serge A R B

2018-01-01

Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

A comparison and appraisal of a comprehensive range of human thermal climate indices

NASA Astrophysics Data System (ADS)

de Freitas, C. R.; Grigorieva, E. A.

2017-03-01

Numerous human thermal climate indices have been proposed. It is a manifestation of the perceived importance of the thermal environment within the scientific community and a desire to quantify it. Schemes used differ in approach according to the number of variables taken into account, the rationale employed, and the particular design for application. They also vary considerably in type and quality, method used to express output, as well as in several other aspects. In light of this, a three-stage project was undertaken to deliver a comprehensive documentation, classification, and overall evaluation of the full range of existing human thermal climate indices. The first stage of the project produced a comprehensive register of as many thermal indices as could be found, 165 in all. The second stage devised a sorting scheme of these human thermal climate indices that grouped them according to eight primary classification categories. This, the third stage of the project, evaluates the indices. Six evaluation criteria, namely validity, usability, transparency, sophistication, completeness, and scope, are used collectively as evaluation criteria to rate each index scheme. The evaluation criteria are used to assign a score that varies between 1 and 5, 5 being the highest. The indices with the highest in each of the eight primary classification categories are discussed. The work is the final stage of a study of the all human thermal climatic indices that could be found in literature. Others have considered the topic, but this study is the first detailed, genuinely comprehensive, and systematic comparison. The results make it simpler to locate and compare indices. It is now easier for users to reflect on the merits of all available thermal indices and decide which is most suitable for a particular application or investigation.

The Immune System as a Model for Pattern Recognition and Classification

PubMed Central

Carter, Jerome H.

2000-01-01

Objective: To design a pattern recognition engine based on concepts derived from mammalian immune systems. Design: A supervised learning system (Immunos-81) was created using software abstractions of T cells, B cells, antibodies, and their interactions. Artificial T cells control the creation of B-cell populations (clones), which compete for recognition of “unknowns.” The B-cell clone with the “simple highest avidity” (SHA) or “relative highest avidity” (RHA) is considered to have successfully classified the unknown. Measurement: Two standard machine learning data sets, consisting of eight nominal and six continuous variables, were used to test the recognition capabilities of Immunos-81. The first set (Cleveland), consisting of 303 cases of patients with suspected coronary artery disease, was used to perform a ten-way cross-validation. After completing the validation runs, the Cleveland data set was used as a training set prior to presentation of the second data set, consisting of 200 unknown cases. Results: For cross-validation runs, correct recognition using SHA ranged from a high of 96 percent to a low of 63.2 percent. The average correct classification for all runs was 83.2 percent. Using the RHA metric, 11.2 percent were labeled “too close to determine” and no further attempt was made to classify them. Of the remaining cases, 85.5 percent were correctly classified. When the second data set was presented, correct classification occurred in 73.5 percent of cases when SHA was used and in 80.3 percent of cases when RHA was used. Conclusions: The immune system offers a viable paradigm for the design of pattern recognition systems. Additional research is required to fully exploit the nuances of immune computation. PMID:10641961

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

The History of Soil Mapping and Classification in Europe: The role of the European Commission

NASA Astrophysics Data System (ADS)

Montanarella, Luca

2014-05-01

Early systematic soil mapping in Europe dates back to the early times of soil science in the 19th Century and was developed at National scales mostly for taxation purposes. National soil classification systems emerged out of the various scientific communities active at that time in leading countries like Germany, Austria, France, Belgium, United Kingdom and many others. Different scientific communities were leading in the various countries, in some cases stemming from geological sciences, in others as a branch of agricultural sciences. Soil classification for the purpose of ranking soils for their capacity to be agriculturally productive emerged as the main priority, allowing in some countries for very detailed and accurate soil maps at 1:5,000 scale and larger. Detailed mapping was mainly driven by taxation purposes in the early times but evolved in several countries also as a planning and management tool for farms and local administrations. The need for pan-European soil mapping and classification efforts emerged only after World War II in the early 1950's under the auspices of FAO with the aim to compile a common European soil map as a contribution to the global soil mapping efforts of FAO at that time. These efforts evolved over the next decades, with the support of the European Commission, towards the establishment of a permanent network of National soil survey institutions (the European Soil Bureau Network). With the introduction of digital soil mapping technologies, the new European Soil Information System (EUSIS) was established, incorporating data at multiple scales for the EU member states and bordering countries. In more recent years, the formal establishment of the European Soil Data Centre (ESDAC) hosted by the European Commission, together with a formal legal framework for soil mapping and soil classification provided by the INSPIRE directive and the related standardization and harmonization efforts, has led to the operational development of advanced digital soil mapping techniques supporting the contribution of Europe to a common global soil information system under the coordination of the Global Soil Partnership (GSP) of FAO. Further information: http://eusoils.jrc.ec.europa.eu/ References: Mark G Kibblewhite, Ladislav Miko, Luca Montanarella, Legal frameworks for soil protection: current development and technical information requirements, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 573-577. Luca Montanarella, Ronald Vargas, Global governance of soil resources as a necessary condition for sustainable development, Current Opinion in Environmental Sustainability, Volume 4, Issue 5, November 2012, Pages 559-564.

Classifying Adverse Events in the Dental Office.

PubMed

Kalenderian, Elsbeth; Obadan-Udoh, Enihomo; Maramaldi, Peter; Etolue, Jini; Yansane, Alfa; Stewart, Denice; White, Joel; Vaderhobli, Ram; Kent, Karla; Hebballi, Nutan B; Delattre, Veronique; Kahn, Maria; Tokede, Oluwabunmi; Ramoni, Rachel B; Walji, Muhammad F

2017-06-30

Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office. Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications. A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient. Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office.

Prescriptions for self-injectable epinephrine and follow-up referral in emergency department patients presenting with anaphylaxis.

PubMed

Campbell, Ronna L; Luke, Anuradha; Weaver, Amy L; St Sauver, Jennifer L; Bergstralh, Eric J; Li, James T; Manivannan, Veena; Decker, Wyatt W

2008-12-01

Anaphylaxis guidelines recommend that patients with a history of anaphylactic reaction should carry self-injectable epinephrine and should be referred to an allergist. To evaluate how frequently patients dismissed from the emergency department after treatment for anaphylaxis received a prescription for self-injectable epinephrine or allergist referral. A retrospective medical record review identified patients with anaphylaxis in a community-based study from 1990 through 2000. Records of patients with Hospital Adaptation of the International Classification of Diseases, Second Edition or International Classification of Diseases, Ninth Revision codes representing anaphylaxis were reviewed, and a random sample of patients with associated diagnoses was also reviewed. Patients who met the criteria for diagnosis of anaphylaxis were included in the study. Among 208 patients identified with anaphylaxis, 134 (64.4%) were seen in the emergency department and discharged home. On dismissal, 49 patients (36.6%; 95% confidence interval [CI], 28.4%-44.7%) were prescribed self-injectable epinephrine, and 42 patients (31.3%; 95% CI, 23.5%-39.2%) were referred to an allergist. Treatment with epinephrine in the emergency department (odds ratio, 3.6; 95% CI, 1.6-7.9; P = .001) and insect sting as the inciting allergen (odds ratio, 4.0; 95% CI, 1.6-10.5; P = .004) were significantly associated with receiving a prescription for self-injectable epinephrine. Patient age younger than 18 years was the only factor associated with referral to an allergist (P = .007). Most patients dismissed after treatment for anaphylaxis did not receive a self-injectable epinephrine prescription or allergist referral. Emergency physicians may be missing an important opportunity to ensure prompt treatment of future anaphylactic reactions and specialized follow-up care.

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping

PubMed Central

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-01-01

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply. PMID:27128915

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

PubMed

Chuang, Yung-Chung Matt; Shiu, Yi-Shiang

2016-04-26

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.

Fuzzy logic modeling of bioaccumulation pattern of metals in coastal biota of Ondo State, Nigeria.

PubMed

Agunbiade, Foluso O; Olu-Owolabi, Bamidele I; Adebowale, Kayode O

2012-01-01

The accumulation patterns of ten metals in tissues of plant, Eichornia crassipes, and fishes, Hydrocynus forskahlii and Oreochromis mossambicus, were modeled with simple fuzzy classification (SFC) to assess toxic effects of anthropogenic activities on the coastal biota. The plant sample was separated into root, stem, and leaves and the fishes into bones, internal tissues, and muscles. They were analyzed for As, Cd, Cr, Cu, Ni, Pb, V, Fe, Mn, and Zn after wet oxidation of their dried samples. The results were converted into membership functions of five accumulation classes and aggregated with SFC. The classification results showed that there was no metal accumulation in the plant parts while the fishes were classified into low accumulation category. The internal tissues of the fishes had higher metal accumulation than the other parts. Generally, Fe and Mn had highest concentrations in the biota but are natural to the area and may not constitute significant risk. Cr had the highest transfer and accumulation from the coastal water into the aquatic lives and may be indicative of risk prone system being a toxic metal. Metal contaminations in the zone had not significantly accumulated in the biota making them less prone to risk associated with metal accumulation.

A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

PubMed Central

Hajduk, Joanna; Klupczynska, Agnieszka; Dereziński, Paweł; Matysiak, Jan; Kokot, Piotr; Nowak, Dorota M.; Gajęcka, Marzena; Nowak-Markwitz, Ewa; Kokot, Zenon J.

2015-01-01

The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18) and a matched control group (n = 13). The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, l-citrulline, l-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44%) and specificity (84.62%), as well as the total group membership classification value (90.32%) calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases. PMID:26694367

Agreement between TOAST and CCS ischemic stroke classification: the NINDS SiGN study.

PubMed

McArdle, Patrick F; Kittner, Steven J; Ay, Hakan; Brown, Robert D; Meschia, James F; Rundek, Tatjana; Wassertheil-Smoller, Sylvia; Woo, Daniel; Andsberg, Gunnar; Biffi, Alessandro; Brenner, David A; Cole, John W; Corriveau, Roderick; de Bakker, Paul I W; Delavaran, Hossein; Dichgans, Martin; Grewal, Raji P; Gwinn, Katrina; Huq, Mohammed; Jern, Christina; Jimenez-Conde, Jordi; Jood, Katarina; Kaplan, Robert C; Katschnig, Petra; Katsnelson, Michael; Labovitz, Daniel L; Lemmens, Robin; Li, Linxin; Lindgren, Arne; Markus, Hugh S; Peddareddygari, Leema R; Pedersén, Annie; Pera, Joanna; Redfors, Petra; Roquer, Jaume; Rosand, Jonathan; Rost, Natalia S; Rothwell, Peter M; Sacco, Ralph L; Sharma, Pankaj; Slowik, Agnieszka; Sudlow, Cathie; Thijs, Vincent; Tiedt, Steffen; Valenti, Raffaella; Worrall, Bradford B

2014-10-28

The objective of this study was to assess the level of agreement between stroke subtype classifications made using the Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS) systems. Study subjects included 13,596 adult men and women accrued from 20 US and European genetic research centers participating in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN). All cases had independently classified TOAST and CCS stroke subtypes. Kappa statistics were calculated for the 5 major ischemic stroke subtypes common to both systems. The overall agreement between TOAST and CCS was moderate (agreement rate, 70%; κ = 0.59, 95% confidence interval [CI] 0.58-0.60). Agreement varied widely across study sites, ranging from 28% to 90%. Agreement on specific subtypes was highest for large-artery atherosclerosis (κ = 0.71, 95% CI 0.69-0.73) and lowest for small-artery occlusion (κ = 0.56, 95% CI 0.54-0.58). Agreement between TOAST and CCS diagnoses was moderate. Caution is warranted when comparing or combining results based on the 2 systems. Replication of study results, for example, genome-wide association studies, should utilize phenotypes determined by the same classification system, ideally applied in the same manner. © 2014 American Academy of Neurology.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

PubMed

Adetiba, Emmanuel; Olugbara, Oludayo O

2015-01-01

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Links between early baseline cortisol, attachment classification, and problem behaviors: A test of differential susceptibility versus diathesis-stress.

PubMed

Fong, Michelle C; Measelle, Jeffrey; Conradt, Elisabeth; Ablow, Jennifer C

2017-02-01

The purpose of the current study was to predict concurrent levels of problem behaviors from young children's baseline cortisol and attachment classification, a proxy for the quality of caregiving experienced. In a sample of 58 children living at or below the federal poverty threshold, children's baseline cortisol levels, attachment classification, and problem behaviors were assessed at 17 months of age. We hypothesized that an interaction between baseline cortisol and attachment classification would predict problem behaviors above and beyond any main effects of baseline cortisol and attachment. However, based on limited prior research, we did not predict whether or not this interaction would be more consistent with diathesis-stress or differential susceptibility models. Consistent with diathesis-stress theory, the results indicated no significant differences in problem behavior levels among children with high baseline cortisol. In contrast, children with low baseline cortisol had the highest level of problem behaviors in the context of a disorganized attachment relationship. However, in the context of a secure attachment relationship, children with low baseline cortisol looked no different, with respect to problem behavior levels, then children with high cortisol levels. These findings have substantive implications for the socioemotional development of children reared in poverty. Copyright © 2017 Elsevier Inc. All rights reserved.

Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions

PubMed Central

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-01-01

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions. PMID:24811079

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning

PubMed Central

Cohen, Kevin Bretonnel; Glass, Benjamin; Greiner, Hansel M.; Holland-Bouley, Katherine; Standridge, Shannon; Arya, Ravindra; Faist, Robert; Morita, Diego; Mangano, Francesco; Connolly, Brian; Glauser, Tracy; Pestian, John

2016-01-01

Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient’s status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral. PMID:27257386

Visual modifications on the P300 speller BCI paradigm

NASA Astrophysics Data System (ADS)

Salvaris, M.; Sepulveda, F.

2009-08-01

The best known P300 speller brain-computer interface (BCI) paradigm is the Farwell and Donchin paradigm. In this paper, various changes to the visual aspects of this protocol are explored as well as their effects on classification. Changes to the dimensions of the symbols, the distance between the symbols and the colours used were tested. The purpose of the present work was not to achieve the highest possible accuracy results, but to ascertain whether these simple modifications to the visual protocol will provide classification differences between them and what these differences will be. Eight subjects were used, with each subject carrying out a total of six different experiments. In each experiment, the user spelt a total of 39 characters. Two types of classifiers were trained and tested to determine whether the results were classifier dependant. These were a support vector machine (SVM) with a radial basis function (RBF) kernel and Fisher's linear discriminant (FLD). The single-trial classification results and multiple-trial classification results were recorded and compared. Although no visual protocol was the best for all subjects, the best performances, across both classifiers, were obtained with the white background (WB) visual protocol. The worst performance was obtained with the small symbol size (SSS) visual protocol.

Global land cover mapping: a review and uncertainty analysis

USGS Publications Warehouse

Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

2014-01-01

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions.

PubMed

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-05-07

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Teacher Leadership: Towards a Research Agenda

ERIC Educational Resources Information Center

Frost, David; Harris, Alma

2003-01-01

This article explores the emerging discourse about teacher leadership in the UK. It draws upon the international literature in exploring a classification of forms of teacher leadership and discusses issues concerned with the policy context. It considers some theoretical perspectives on distributed leadership before going on to examine in detail a…

MYSEA: The Monterey Security Architecture

DTIC Science & Technology

2009-01-01

Security and Protection, Organization and Design General Terms: Design; Security Keywords: access controls, authentication, information flow controls...Applicable environments include: mil- itary coalitions, agencies and organizations responding to security emergencies, and mandated sharing in business ...network architecture affords users the abil- ity to securely access information across networks at dif- ferent classifications using standardized

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

EPA Science Inventory

Background: Exploratory toxicology is a new emerging research area whose ultimate mission is that of protecting human health and environment from risks posed by chemicals. In this regard, the ethical and practical limitation of animal testing has encouraged the promotion of compu...

Taxonomic Proposal: Create sixteen new species in existing genera of the families Alphaflexiviridae and Betaflexiviridae

USDA-ARS?s Scientific Manuscript database

A transparent and functional system for virus classification is essential to allow scientists to correctly identify and report on viruses detected in different hosts or locations without ambiguity. New research into relationships between previously characterized and newly-emerging viruses may requir...

3 new species in a proposed new genus Deltaflexivirus and new family Deltaflexiviridae in the order Tymovirales

USDA-ARS?s Scientific Manuscript database

A transparent and functional system for virus classification is essential to allow scientists to correctly identify and report on viruses detected in different hosts or locations without ambiguity. New research into relationships between previously characterized and newly-emerging viruses may requir...

The role of emergency medicine clerkship e-Portfolio to monitor the learning experience of students in different settings: a prospective cohort study.

PubMed

Cevik, Arif Alper; Shaban, Sami; El Zubeir, Margret; Abu-Zidan, Fikri M

2018-04-12

Although emergency departments provide acute care learning opportunities for medical students, student exposure to recommended curriculum presentations and procedures are limited. In this perspective, clinical environments providing learning opportunities for students should be monitored as part of an ongoing quality improvement process. This study aims to analyze student exposures and their involvement levels in two different hospitals (Tawam and Al Ain) so as to improve the teaching and learning activities. This is a prospective study on all 76 final year medical students' electronic logbooks (e-Portfolio) of the academic year 2016/2017. Students recorded 5087 chief complaints and 3721 procedures. The average patient and procedure exposure in a shift per student in Al Ain Hospital compared with Tawam Hospital were 7.2 vs 6.4 and 5.8 vs 4.3, respectively. The highest full involvement with presentations was seen in the pediatric unit (67.1%, P < 0.0001). Urgent care shifts demonstrated the highest area of "full involvement" with procedures for our students (73.2%, P < 0.0001). Students' highest involvement with presentations and procedures were found during the night shifts (P < 0.0001, 66.5 and 75.1%, respectively). The electronic portfolio has proven to be a very useful tool in defining the learning activities of final year medical students during their emergency medicine clerkship and in comparing activities in two different clinical settings. Data collected and analyzed using this e-Portfolio has the potential to help medical educators and curriculum designers improve emergency medicine teaching and learning activities.

[Nosological classification and assessment of muscle dysmorphia].

PubMed

Babusa, Bernadett; Túry, Ferenc

2011-01-01

Muscle dysmorphia is a recently described psychiatric disorder, characterized by a pathological preoccupation with muscle size. In spite of their huge muscles, muscle dysmorphia sufferers believe that they are insufficiently large and muscular therefore would like to be bigger and more muscular. Male bodybuilders are at high-risk for the disorder. The nosological classification of muscle dysmorphia has been changed over the years. However, consensus has not emerged so far. Most of the ongoing debate has conceptualized muscle dysmorphia as an eating disorder, obsessive-compulsive disorder and body dysmorphic disorder. There are a number of arguments for and againts. In the present study the authors do not take a position on the diagnostic classification of muscle dysmorphia. The purpose of the study is to review the present approaches relating to the diagnostic classification of muscle dysmporphia. Many different questionnaires were developed for the assessment of muscle dysmorphia. Currently, there is a lack of assessment methods measuring muscle dysmorphia symptoms in Hungary. As a secondary purpose the study also presents the Hungarian version of the Muscle Appearance Satisfaction Scale (Mayville et al., 2002).

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

USGS Publications Warehouse

Enwright, Nicholas M.; Hartley, Stephen B.; Couvillion, Brady R.; Michael G. Brasher,; Jenneke M. Visser,; Michael K. Mitchell,; Bart M. Ballard,; Mark W. Parr,; Barry C. Wilson,

2015-07-23

This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

Automated Classification of Asteroids into Families at Work

NASA Astrophysics Data System (ADS)

Knežević, Zoran; Milani, Andrea; Cellino, Alberto; Novaković, Bojan; Spoto, Federica; Paolicchi, Paolo

2014-07-01

We have recently proposed a new approach to the asteroid family classification by combining the classical HCM method with an automated procedure to add newly discovered members to existing families. This approach is specifically intended to cope with ever increasing asteroid data sets, and consists of several steps to segment the problem and handle the very large amount of data in an efficient and accurate manner. We briefly present all these steps and show the results from three subsequent updates making use of only the automated step of attributing the newly numbered asteroids to the known families. We describe the changes of the individual families membership, as well as the evolution of the classification due to the newly added intersections between the families, resolved candidate family mergers, and emergence of the new candidates for the mergers. We thus demonstrate how by the new approach the asteroid family classification becomes stable in general terms (converging towards a permanent list of confirmed families), and in the same time evolving in details (to account for the newly discovered asteroids) at each update.

Clashing Diagnostic Approaches: DSM-ICD versus RDoC

PubMed Central

Lilienfeld, Scott O.; Treadway, Michael T.

2016-01-01

Since at least the middle of the past century, one overarching model of psychiatric classification, namely, that of the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases (DSM-ICD), has reigned supreme. This DSM-ICD approach embraces an Aristotelian view of mental disorders as largely discrete entities that are characterized by distinctive signs, symptoms, and natural histories. Over the past several years, however, a competing vision, namely, the Research Domain Criteria (RDoC) initiative launched by the National Institute of Mental Health, has emerged in response to accumulating anomalies within the DSM-ICD system. In contrast to DSM-ICD, RDoC embraces a Galilean view of psychopathology as the product of dysfunctions in neural circuitry. RDoC appears to be a valuable endeavor that holds out the long-term promise of an alternative system of mental illness classification. We delineate three sets of pressing challenges – conceptual, methodological, and logistical/pragmatic – that must be addressed for RDoC to realize its scientific potential, and conclude with a call for further research, including investigation of a rapprochement between Aristotelian and Galilean approaches to psychiatric classification. PMID:26845519

SYMPTOM PRESENTATIONS AND CLASSIFICATION OF AUTISM SPECTRUM DISORDER IN EARLY CHILDHOOD: APPLICATION TO THE DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD (DC:0-5).

PubMed

Soto, Timothy; Giserman Kiss, Ivy; Carter, Alice S

2016-09-01

Over the past 5 years, a great deal of information about the early course of autism spectrum disorder (ASD) has emerged from longitudinal prospective studies of infants at high risk for developing ASD based on a previously diagnosed older sibling. The current article describes early ASD symptom presentations and outlines the rationale for defining a new disorder, Early Atypical Autism Spectrum Disorder (EA-ASD) to accompany ASD in the new revision of the ZERO TO THREE Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0-5) (in press) alternative diagnostic classification manual. EA-ASD is designed to identify children who are 9 to 36 months of age presenting with a minimum of (a) two social-communication symptoms and (b) one repetitive and restricted behavior symptom as well as (c) evidence of impairment, with the intention of providing these children with appropriately tailored services and improving the likelihood of optimizing their development. © 2016 Michigan Association for Infant Mental Health.

SYMPTOM PRESENTATIONS AND CLASSIFICATION OF AUTISM SPECTRUM DISORDER IN EARLY CHILDHOOD: APPLICATION TO THE DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD (DC:0–5)

PubMed Central

SOTO, TIMOTHY; KISS, IVY GISERMAN; CARTER, ALICE S.

2018-01-01

Over the past 5 years, a great deal of information about the early course of autism spectrum disorder (ASD) has emerged from longitudinal prospective studies of infants at high risk for developing ASD based on a previously diagnosed older sibling. The current article describes early ASD symptom presentations and outlines the rationale for defining a new disorder, Early Atypical Autism Spectrum Disorder (EA-ASD) to accompany ASD in the new revision of the ZERO TO THREE Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0–5) (in press) alternative diagnostic classification manual. EA-ASD is designed to identify children who are 9 to 36 months of age presenting with a minimum of (a) two social-communication symptoms and (b) one repetitive and restricted behavior symptom as well as (c) evidence of impairment, with the intention of providing these children with appropriately tailored services and improving the likelihood of optimizing their development. PMID:27556740

An admissible level \\widehat{osp} ( 1 \\big \\vert 2 ) -model: modular transformations and the Verlinde formula

NASA Astrophysics Data System (ADS)

Snadden, John; Ridout, David; Wood, Simon

2018-05-01

The modular properties of the simple vertex operator superalgebra associated with the affine Kac-Moody superalgebra \\widehat{{osp}} (1|2) at level -5/4 are investigated. After classifying the relaxed highest-weight modules over this vertex operator superalgebra, the characters and supercharacters of the simple weight modules are computed and their modular transforms are determined. This leads to a complete list of the Grothendieck fusion rules by way of a continuous superalgebraic analog of the Verlinde formula. All Grothendieck fusion coefficients are observed to be non-negative integers. These results indicate that the extension to general admissible levels will follow using the same methodology once the classification of relaxed highest-weight modules is completed.

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 Machine Learning-based Method for Question Type Classification in Biomedical Question Answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-05-18

Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary. In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers. Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features' provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %. The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

Measuring elimination of podoconiosis, endemicity classifications, case definition and targets: an international Delphi exercise.

PubMed

Deribe, Kebede; Wanji, Samuel; Shafi, Oumer; Muheki Tukahebwa, Edridah; Umulisa, Irenee; Davey, Gail

2015-09-01

Podoconiosis is one of the major causes of lymphoedema in the tropics. Nonetheless, currently there are no endemicity classifications or elimination targets to monitor the effects of interventions. This study aimed at establishing case definitions and indicators that can be used to assess endemicity, elimination and clinical outcomes of podoconiosis. This paper describes the result of a Delphi technique used among 28 experts. A questionnaire outlining possible case definitions, endemicity classifications, elimination targets and clinical outcomes was developed. The questionnaire was distributed to experts working on podoconiosis and other neglected tropical diseases in two rounds. The experts rated the importance of case definitions, endemic classifications, elimination targets and the clinical outcome measures. Median and mode were used to describe the central tendency of expert responses. The coefficient of variation was used to describe the dispersals of expert responses. Consensus on definitions and indicators for assessing endemicity, elimination and clinical outcomes of podoconiosis directed at policy makers and health workers was achieved following the two rounds of Delphi approach among the experts. Based on the two Delphi rounds we discuss potential indicators and endemicity classification of this disabling disease, and the ongoing challenges to its elimination in countries with the highest prevalence. Consensus will help to increase effectiveness of podoconiosis elimination efforts and ensure comparability of outcome data. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Qualitative pattern classification of shear wave elastography for breast masses: how it correlates to quantitative measurements.

PubMed

Yoon, Jung Hyun; Ko, Kyung Hee; Jung, Hae Kyoung; Lee, Jong Tae

2013-12-01

To determine the correlation of qualitative shear wave elastography (SWE) pattern classification to quantitative SWE measurements and whether it is representative of quantitative SWE values with similar performances. From October 2012 to January 2013, 267 breast masses of 236 women (mean age: 45.12 ± 10.54 years, range: 21-88 years) who had undergone ultrasonography (US), SWE, and subsequent biopsy were included. US BI-RADS final assessment and qualitative and quantitative SWE measurements were recorded. Correlation between pattern classification and mean elasticity, maximum elasticity, elasticity ratio and standard deviation were evaluated. Diagnostic performances of grayscale US, SWE parameters, and US combined to SWE values were calculated and compared. Of the 267 breast masses, 208 (77.9%) were benign and 59 (22.1%) were malignant. Pattern classifications significantly correlated with all quantitative SWE measurements, showing highest correlation with maximum elasticity, r = 0.721 (P<0.001). Sensitivity was significantly decreased in US combined to SWE measurements to grayscale US: 69.5-89.8% to 100.0%, while specificity was significantly improved: 62.5-81.7% to 13.9% (P<0.001). Area under the ROC curve (Az) did not show significant differences between grayscale US to US combined to SWE (P>0.05). Pattern classification shows high correlation to maximum stiffness and may be representative of quantitative SWE values. When combined to grayscale US, SWE improves specificity of US. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Preoperative risk assessment with the ASA classification. A prospective study of morbidity and mortality in various ASA classes in 2,937 patients in general surgery].

PubMed

Menke, H; John, K D; Klein, A; Lorenz, W; Junginger, T

1992-12-01

The value of ASA classification in assessment of perioperative risk, i.e. especially postoperative morbidity, was analyzed prospectively using the data of 2937 patients. The analysis took into account the criteria validity, reliability, and sensitivity. The incidence of post-operative morbidity after elective surgery rose from 3.9% in ASA class I to 36% in ASA class IV. Mortality was 0.6% in ASA class II, whereas 9.3% died in ASA class IV. Morbidity, mortality respectively, after emergency surgery was 10.2% in ASA class II compared to 69% in class IV, mortality 1.4% compared to 21.5%. Differences between the ASA classes were confirmed (p-value < 0.05) considering separate kinds of complications and different periods. Furthermore, ASA classification was a valuable reference to length of stay and severity of necessary therapy at the ICU.

[BASIC PRINCIPLES AND METHODS TO THE USE IMMUNOMODULATING PREPARATIONS IN CLINICAL PRACTICE: CLASSIFICATION, INDICATIONS AND CONTRAINDICATIONS].

PubMed

Sepiashvili, R

2015-06-01

This paper is devoted to one of the most pressing issues in modern clinical medicine, the problem of immunomodulators and immunotropic therapy. The materials presented are the logical sequel of the papers published by Revaz I. Sepiashvili in 2001-2015. In these articles, the author proposed the first classification of immunotropic preparations, a brief historical background and chronological emergence of the concept of therapies, as well as definition of immunomodulators. This paper presents an updated classification of immunomodulatory drugs which is valid for January 2015. The paper also outlines basic principles for therapies that allow the clinician not only to select a proper immunomodulator but also to develop strategy and tactics in treatment of the patient, taking into account his/her individual characteristics and the need to use in clinical practice only officially registered immunotropic preparations.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

Cell-based therapy technology classifications and translational challenges

PubMed Central

Mount, Natalie M.; Ward, Stephen J.; Kefalas, Panos; Hyllner, Johan

2015-01-01

Cell therapies offer the promise of treating and altering the course of diseases which cannot be addressed adequately by existing pharmaceuticals. Cell therapies are a diverse group across cell types and therapeutic indications and have been an active area of research for many years but are now strongly emerging through translation and towards successful commercial development and patient access. In this article, we present a description of a classification of cell therapies on the basis of their underlying technologies rather than the more commonly used classification by cell type because the regulatory path and manufacturing solutions are often similar within a technology area due to the nature of the methods used. We analyse the progress of new cell therapies towards clinical translation, examine how they are addressing the clinical, regulatory, manufacturing and reimbursement requirements, describe some of the remaining challenges and provide perspectives on how the field may progress for the future. PMID:26416686

Auditory hallucinations: nomenclature and classification.

PubMed

Blom, Jan Dirk; Sommer, Iris E C

2010-03-01

The literature on the possible neurobiologic correlates of auditory hallucinations is expanding rapidly. For an adequate understanding and linking of this emerging knowledge, a clear and uniform nomenclature is a prerequisite. The primary purpose of the present article is to provide an overview of the nomenclature and classification of auditory hallucinations. Relevant data were obtained from books, PubMed, Embase, and the Cochrane Library. The results are presented in the form of several classificatory arrangements of auditory hallucinations, governed by the principles of content, perceived source, perceived vivacity, relation to the sleep-wake cycle, and association with suspected neurobiologic correlates. This overview underscores the necessity to reappraise the concepts of auditory hallucinations developed during the era of classic psychiatry, to incorporate them into our current nomenclature and classification of auditory hallucinations, and to test them empirically with the aid of the structural and functional imaging techniques currently available.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.

Objective breast tissue image classification using Quantitative Transmission ultrasound tomography

NASA Astrophysics Data System (ADS)

Malik, Bilal; Klock, John; Wiskin, James; Lenox, Mark

2016-12-01

Quantitative Transmission Ultrasound (QT) is a powerful and emerging imaging paradigm which has the potential to perform true three-dimensional image reconstruction of biological tissue. Breast imaging is an important application of QT and allows non-invasive, non-ionizing imaging of whole breasts in vivo. Here, we report the first demonstration of breast tissue image classification in QT imaging. We systematically assess the ability of the QT images’ features to differentiate between normal breast tissue types. The three QT features were used in Support Vector Machines (SVM) classifiers, and classification of breast tissue as either skin, fat, glands, ducts or connective tissue was demonstrated with an overall accuracy of greater than 90%. Finally, the classifier was validated on whole breast image volumes to provide a color-coded breast tissue volume. This study serves as a first step towards a computer-aided detection/diagnosis platform for QT.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages

PubMed Central

Cronin, Robert M.; Fabbri, Daniel; Denny, Joshua C.; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804–0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs. PMID:26958285

Emerging Issues in Virus Taxonomy

PubMed Central

Mahy, Brian W.J.

2004-01-01

Viruses occupy a unique position in biology. Although they possess some of the properties of living systems such as having a genome, they are actually nonliving infectious entities and should not be considered microorganisms. A clear distinction should be drawn between the terms virus, virion, and virus species. Species is the most fundamental taxonomic category used in all biological classification. In 1991, the International Committee on Taxonomy of Viruses (ICTV) decided that the category of virus species should be used in virus classification together with the categories of genus and family. More than 50 ICTV study groups were given the task of demarcating the 1,550 viral species that were recognized in the 7th ICTV report, which was published in 2000. We briefly describe the changes in virus classification that were introduced in that report. We also discuss recent proposals to introduce a nonlatinized binomial nomenclature for virus species. PMID:15078590

Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

NASA Technical Reports Server (NTRS)

Cibula, W. G.

1981-01-01

Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

Efficient Resource Utilization in the Bayne-Jones Army Community Hospital Emergency Room

DTIC Science & Technology

1990-08-07

34 Previous editions are obsolete. SECURITY CLASSIFICATION OF THIS PAGE MI 9e9TEbc9$ff c6 Apxv~ k puUilt, -t 2A m BOCK #19 (continued) IT on ER patients...performed by nurses. American Journal of Public Health, 1989 Edition . (1989). Chicago: American Hospital Association. American College of Emergency Physicians... 6th Ed. New York: McGraw-Hill Book Company. Liptak, G.S., Super, D.M., Baker, N., & Roghmann, K.J. (1985). An analysis of waiting times in a pediatric

Micro-bias and macro-performance.

PubMed

Seaver, S M D; Moreira, A A; Sales-Pardo, M; Malmgren, R D; Diermeier, D; Amaral, L A N

2009-02-01

We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task - a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations' ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs.

Using patient classification systems to identify ambulatory care costs.

PubMed

Karpiel, M S

1994-11-01

Ambulatory care continues to increase as a percentage of total hospital revenue. Until recently, reimbursement for ambulatory care was provided on a cost basis. However, payers are attempting to exert more control over reimbursement for ambulatory care. The Health Care Financing Administration, for example, is expanding the use of prospective payment to cover more forms of outpatient care. Thus, in order to ensure the financial viability of their organizations, healthcare financial managers will need cost-accounting tools, such as patient classification systems, to ascertain the direct and indirect costs of emergency or outpatient visits and thereby to refine pricing, contracting, staffing, productivity, and profitability analyses for ambulatory care.

Extreme Facial Expressions Classification Based on Reality Parameters

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Rad, Abdolvahab Ehsani; Rehman, Amjad; Altameem, Ayman

2014-09-01

Extreme expressions are really type of emotional expressions that are basically stimulated through the strong emotion. An example of those extreme expression is satisfied through tears. So to be able to provide these types of features; additional elements like fluid mechanism (particle system) plus some of physics techniques like (SPH) are introduced. The fusion of facile animation with SPH exhibits promising results. Accordingly, proposed fluid technique using facial animation is the real tenor for this research to get the complex expression, like laugh, smile, cry (tears emergence) or the sadness until cry strongly, as an extreme expression classification that's happens on the human face in some cases.

Characteristics of the Highest Users of Emergency Services in Veterans Affairs Hospitals: Homeless and Non-Homeless.

PubMed

Gundlapalli, Adi V; Jones, Audrey L; Redd, Andrew; Suo, Ying; Pettey, Warren B P; Mohanty, April; Brignone, Emily; Gawron, Lori; Vanneman, Megan; Samore, Matthew H; Fargo, Jamison D

2017-01-01

Efforts are underway to understand recent increases in emergency department (ED) use and to offer case management to those patients identified as high utilizers. Homeless Veterans are thought to use EDs for non-emergent conditions. This study identifies the highest users of ED services in the Department of Veterans Affairs and provides descriptive analyses of these Veterans, the diagnoses for which they were seen in the ED, and differences based on their homeless status. Homeless Veterans were more likely than non-homeless Veterans to have >10 visits in the 2014 calendar year (12% vs. <1%). Homeless versus non-homeless Veterans with >10 visits were more often male, <age 60, and non-married. Non-homeless Veterans with >10 ED visits were often treated for chest and abdominal pain, and back problems, whereas homeless Veterans were frequently treated for mental health/substance use. Tailored case management approaches may be needed to better link homeless Veterans with high ED use to appropriate outpatient care.

Patterns and average rates of late Neogene Recent uplift of the Betic Cordillera, SE Spain

NASA Astrophysics Data System (ADS)

Braga, Juan C.; Martín, José M.; Quesada, Cecilio

2003-02-01

The facies distribution in the sedimentary units infilling a series of Neogene basins has been used to reconstruct the relief generation and uplift across the Internal Zone of the Betic Cordillera in southern Spain. Uplift amounts and average rates can be estimated using the current elevation of the outcrops of well-dated deposits indicative of ancient sea-level positions. Coral reefs and coastal conglomerates record the initial development of emergent Betic relief during the Langhian. Continental and marginal marine deposits indicate the existence of a large island centred on the present Sierra Nevada-Sierra de los Filabres chain by the end of the Middle Miocene. The precursor of the Sierra Nevada-Sierra de los Filabres chain, originally part of this large island, remained emerged whilst the surrounding areas were re-invaded by the sea during the early Tortonian. At the end of the Tortonian the inland basins (Granada and Guadix basins) became continental, while the Sierras de la Contraviesa, Sierra de Gádor and Sierra Alhamilla emerged, separating the Alborán Basin from the Alpujarra, Tabernas and Sorbas basins, which became narrow passages of the Mediterranean Sea. In contrast, the Sierra Cabrera emerged during the late Messinian, suggesting a progressive uplift from west to east of the sierras south of the Sierra Nevada-Sierra de los Filabres chain. During the Pliocene, only the low areas closest to the present-day coast remained as marine basins and progressively emerged throughout this stage. The highest average uplift rate recorded is 280 m/Ma for the Sierra de Gádor, although the average uplift rates of upper-Neogene coastal marine rocks since depositon have maximum values of approximately 200 m/Ma. Most of the uplift of the Betic mountains took place before the early Pliocene. The recorded uplift of Neogene rocks was highest at the margins of western Sierra Nevada, where peaks higher than 3000 m occur. The average rates of uplift were lower to the east of this major relief. The main sierras and depressions in the present-day landscape correspond respectively to the emergent land, in which uplift was concentrated, and to the marine basins that existed before the final emergence of the region. The altitude of the sierras reflects the time at which they became emergent, the highest mountains being the first to rise above sea level.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

PubMed

Rashid, Nasir; Iqbal, Javaid; Javed, Amna; Tiwana, Mohsin I; Khan, Umar Shahbaz

2018-01-01

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Investigating the Capability of IRS-P6-LISS IV Satellite Image for Pistachio Forests Density Mapping (case Study: Northeast of Iran)

NASA Astrophysics Data System (ADS)

Hoseini, F.; Darvishsefat, A. A.; Zargham, N.

2012-07-01

In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in an area of 500 ha in Iran. After geometric correction, suitable training areas were determined based on fieldwork. Suitable spectral transformations like NDVI, PVI and PCA were performed. A ground truth map included of 34 plots (each plot 1 ha) were prepared. Hard and soft supervised classifications were performed with 5 density classes (0-5%, 5-10%, 10-15%, 15-20% and > 20%). Because of low separability of classes, some classes were merged and classifications were repeated with 3 classes. Finally, the highest overall accuracy and kappa coefficient of 70% and 0.44, respectively, were obtained with three classes (0-5%, 5-20%, and > 20%) by fuzzy classifier. Considering the low kappa value obtained, it could be concluded that the result of the classification was not desirable. Therefore, this approach is not appropriate for operational mapping of these valuable Pistachio forests.

Incidence of sports and recreation related injuries resulting in hospitalization in Wisconsin in 2000

PubMed Central

Dempsey, R; Layde, P; Laud, P; Guse, C; Hargarten, S

2005-01-01

Objective: To describe the incidence and patterns of sports and recreation related injuries resulting in inpatient hospitalization in Wisconsin. Although much sports and recreation related injury research has focused on the emergency department setting, little is known about the scope or characteristics of more severe sports injuries resulting in hospitalization. Setting: The Wisconsin Bureau of Health Information (BHI) maintains hospital inpatient discharge data through a statewide mandatory reporting system. The database contains demographic and health information on all patients hospitalized in acute care non-federal hospitals in Wisconsin. Methods: The authors developed a classification scheme based on the International Classification of Diseases External cause of injury code (E code) to identify hospitalizations for sports and recreation related injuries from the BHI data files (2000). Due to the uncertainty within E codes in specifying sports and recreation related injuries, the authors used Bayesian analysis to model the incidence of these types of injuries. Results: There were 1714 (95% credible interval 1499 to 2022) sports and recreation-related injury hospitalizations in Wisconsin in 2000 (32.0 per 100 000 population). The most common mechanisms of injury were being struck by/against an object in sports (6.4 per 100 000 population) and pedal cycle riding (6.2 per 100 000). Ten to 19 year olds had the highest rate of sports and recreation related injury hospitalization (65.3 per 100 000 population), and males overall had a rate four times higher than females. Conclusions: Over 1700 sports and recreation related injuries occurred in Wisconsin in 2000 that were treated during an inpatient hospitalization. Sports and recreation activities result in a substantial number of serious, as well as minor injuries. Prevention efforts aimed at reducing injuries while continuing to promote participation in physical activity for all ages are critical. PMID:15805437

Vulnerability of Internally Displaced Children in Disaster Relief Camps of Pakistan: Issues, Challenges, and Way Forward

ERIC Educational Resources Information Center

Hirani, Shela Akbar Ali

2014-01-01

Pakistan is a developing country with the second highest infant and child mortality rates in South Asia. During the past years this region has undergone several humanitarian emergencies that have negatively affected all the aspects of health and development of young children. During these emergencies relief camps are set up by governmental and…

Pros and cons of conjoint analysis of discrete choice experiments to define classification and response criteria in rheumatology.

PubMed

Taylor, William J

2016-03-01

Conjoint analysis of choice or preference data has been used in marketing for over 40 years but has appeared in healthcare settings much more recently. It may be a useful technique for applications within the rheumatology field. Conjoint analysis in rheumatology contexts has mainly used the approaches implemented in 1000Minds Ltd, Dunedin, New Zealand, Sawtooth Software, Orem UT, USA. Examples include classification criteria, composite response criteria, service prioritization tools and utilities assessment. Limitations imposed by very many attributes can be managed using new techniques. Conjoint analysis studies of classification and response criteria suggest that the assumption of equal weighting of attributes cannot be met, which challenges traditional approaches to composite criteria construction. Weights elicited through choice experiments with experts can derive more accurate classification criteria, than unweighted criteria. Studies that find significant variation in attribute weights for composite response criteria for gout make construction of such criteria problematic. Better understanding of various multiattribute phenomena is likely to increase with increased use of conjoint analysis, especially when the attributes concern individual perceptions or opinions. In addition to classification criteria, some applications for conjoint analysis that are emerging in rheumatology include prioritization tools, remission criteria, and utilities for life areas.

Which method of posttraumatic stress disorder classification best predicts psychosocial function in children with traumatic brain injury?

PubMed

Iselin, Greg; Le Brocque, Robyne; Kenardy, Justin; Anderson, Vicki; McKinlay, Lynne

2010-10-01

Controversy surrounds the classification of posttraumatic stress disorder (PTSD), particularly in children and adolescents with traumatic brain injury (TBI). In these populations, it is difficult to differentiate TBI-related organic memory loss from dissociative amnesia. Several alternative PTSD classification algorithms have been proposed for use with children. This paper investigates DSM-IV-TR and alternative PTSD classification algorithms, including and excluding the dissociative amnesia item, in terms of their ability to predict psychosocial function following pediatric TBI. A sample of 184 children aged 6-14 years were recruited following emergency department presentation and/or hospital admission for TBI. PTSD was assessed via semi-structured clinical interview (CAPS-CA) with the child at 3 months post-injury. Psychosocial function was assessed using the parent report CHQ-PF50. Two alternative classification algorithms, the PTSD-AA and 2 of 3 algorithms, reached statistical significance. While the inclusion of the dissociative amnesia item increased prevalence rates across algorithms, it generally resulted in weaker associations with psychosocial function. The PTSD-AA algorithm appears to have the strongest association with psychosocial function following TBI in children and adolescents. Removing the dissociative amnesia item from the diagnostic algorithm generally results in improved validity. Copyright 2010 Elsevier Ltd. All rights reserved.

[Scientific publication output of Spanish emergency physicians from 2005 to 2014: a comparative study].

PubMed

Fernández-Guerrero, Inés María; Martín-Sánchez, Francisco Javier; Burillo-Putze, Guillermo; Miró, Òscar

2017-10-01

To analyze the research output of Spanish emergency physicians between 2005 and 2014 and to compare it to their output in the previous 10-year period (1995-2004) as well as to that of emergency physicians in other countries and Spanish physicians in other specialties. Original articles indexed in the Science Citation Index Expanded of the Web of Science were included. Documents from Spanish emergency physicians were identified by combining the word Spain and any other search term identifying an emergency service or unit in Spain. To identify articles from 7 other Spanish specialties (hematology, endocrinology, cardiology, pneumology, digestive medicine, pediatrics, surgery and orthopedic medicine or traumatology) and emergency physicians in 8 other countries (United States, United Kingdom, Ireland, Italy, France, Germany, Netherlands, Belgium) we used similar strategies. Information about production between 1995 and 2004 was extracted from a prior publication. Spanish emergency physicians signed 1254 articles (mean [SD], 125 [44] articles/y) between 2005 and 2014. That level of productivity was greater than in the 1995-2004 period (mean, 26 [14] articles/y), although the annual growth rate fell from 12.5% in the previous 10-year period to 5.2% in the most recent one. Emergency medicine was among the least productive Spanish specialties we studied, but our discipline's annual growth rate of 5.2% was the highest. Spanish emergency medicine occupies an intermediate position (ranking fifth) among the 9 countries studied, although the population-adjusted rank was higher (fourth). When output was adjusted for gross domestic product, Spain climbed higher in rank, to second position. The annual growth rate was the fourth highest among countries, after Germany (9.9%), the Netherlands (7.3%), and Italy (6.0%). The research output of Spanish emergency physicians continues to be quantitatively lower than that of other Spanish specialties and of emergency physicians in other countries. The annual rate of growth in publications, although good, fell below the growth rate of the previous period.

Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity

PubMed Central

Geng, Xiangfei; Xu, Junhai; Liu, Baolin; Shi, Yonggang

2018-01-01

Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention. PMID:29515348

Quantitative computed tomography applied to interstitial lung diseases.

PubMed

Obert, Martin; Kampschulte, Marian; Limburg, Rebekka; Barańczuk, Stefan; Krombach, Gabriele A

2018-03-01

To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15 th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R 2 (NR 2 ) effect size were estimated. NR 2 was used to set up a ranking list of the different methods. MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR 2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR 2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR 2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR 2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR 2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR 2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR 2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR 2 0.48). The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Articular cartilage degeneration classification by means of high-frequency ultrasound.

PubMed

Männicke, N; Schöne, M; Oelze, M; Raum, K

2014-10-01

To date only single ultrasound parameters were regarded in statistical analyses to characterize osteoarthritic changes in articular cartilage and the potential benefit of using parameter combinations for characterization remains unclear. Therefore, the aim of this work was to utilize feature selection and classification of a Mankin subset score (i.e., cartilage surface and cell sub-scores) using ultrasound-based parameter pairs and investigate both classification accuracy and the sensitivity towards different degeneration stages. 40 punch biopsies of human cartilage were previously scanned ex vivo with a 40-MHz transducer. Ultrasound-based surface parameters, as well as backscatter and envelope statistics parameters were available. Logistic regression was performed with each unique US parameter pair as predictor and different degeneration stages as response variables. The best ultrasound-based parameter pair for each Mankin subset score value was assessed by highest classification accuracy and utilized in receiver operating characteristics (ROC) analysis. The classifications discriminating between early degenerations yielded area under the ROC curve (AUC) values of 0.94-0.99 (mean ± SD: 0.97 ± 0.03). In contrast, classifications among higher Mankin subset scores resulted in lower AUC values: 0.75-0.91 (mean ± SD: 0.84 ± 0.08). Variable sensitivities of the different ultrasound features were observed with respect to different degeneration stages. Our results strongly suggest that combinations of high-frequency ultrasound-based parameters exhibit potential to characterize different, particularly very early, degeneration stages of hyaline cartilage. Variable sensitivities towards different degeneration stages suggest that a concurrent estimation of multiple ultrasound-based parameters is diagnostically valuable. In-vivo application of the present findings is conceivable in both minimally invasive arthroscopic ultrasound and high-frequency transcutaneous ultrasound. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions.

PubMed

Lukas, Vanessa A; Fishbein, Kenneth W; Reiter, David A; Lin, Ping-Chang; Schneider, Erika; Spencer, Richard G

2015-07-01

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images. Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC). The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies. Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules. © 2014 Wiley Periodicals, Inc.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Methods of classification for women undergoing induction of labour: a systematic review and novel classification system.

PubMed

Nippita, T A; Khambalia, A Z; Seeho, S K; Trevena, J A; Patterson, J A; Ford, J B; Morris, J M; Roberts, C L

2015-09-01

A lack of reproducible methods for classifying women having an induction of labour (IOL) has led to controversies regarding IOL and related maternal and perinatal health outcomes. To evaluate articles that classify IOL and to develop a novel IOL classification system. Electronic searches using CINAHL, EMBASE, WEB of KNOWLEDGE, and reference lists. Two reviewers independently assessed studies that classified women having an IOL. For the systematic review, data were extracted on study characteristics, quality, and results. Pre-specified criteria were used for evaluation. A multidisciplinary collaboration developed a new classification system using a clinically logical model and stakeholder feedback, demonstrating applicability in a population cohort of 909 702 maternities in New South Wales, Australia, over the period 2002-2011. All seven studies included in the systematic review categorised women according to the presence or absence of varying medical indications for IOL. Evaluation identified uncertainties or deficiencies across all studies, related to the criteria of total inclusivity, reproducibility, clinical utility, implementability, and data availability. A classification system of ten groups was developed based on parity, previous caesarean, gestational age, number, and presentation of the fetus. Nulliparous and parous women at full term were the largest groups (21.2 and 24.5%, respectively), and accounted for the highest proportion of all IOL (20.7 and 21.5%, respectively). Current methods of classifying women undertaking IOL based on medical indications are inadequate. We propose a classification system that has the attributes of simplicity and clarity, uses information that is readily and reliably collected, and enables the standard characterisation of populations of women having an IOL across and within jurisdictions. © 2015 Royal College of Obstetricians and Gynaecologists.

Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks

PubMed Central

Ojeda, Lauro; Johnson, Daniel D.; Gates, Deanna; Mower Provost, Emily; Barton, Kira

2018-01-01

Objective Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task. Methods Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset. Results Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69–92%. Conclusion These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications. Significance Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user. PMID:29447252

Atmospheric circulation types and daily mortality in Athens, Greece.

PubMed Central

Kassomenos, P; Gryparis, A; Samoli, E; Katsouyanni, K; Lykoudis, S; Flocas, H A

2001-01-01

We investigated the short-term effects of synoptic and mesoscale atmospheric circulation types on mortality in Athens, Greece. The synoptic patterns in the lower troposphere were classified in 8 a priori defined categories. The mesoscale weather types were classified into 11 categories, using meteorologic parameters from the Athens area surface monitoring network; the daily number of deaths was available for 1987-1991. We applied generalized additive models (GAM), extending Poisson regression, using a LOESS smoother to control for the confounding effects of seasonal patterns. We adjusted for long-term trends, day of the week, ambient particle concentrations, and additional temperature effects. Both classifications, synoptic and mesoscale, explain the daily variation of mortality to a statistically significant degree. The highest daily mortality was observed on days characterized by southeasterly flow [increase 10%; 95% confidence interval (CI), 6.1-13.9% compared to the high-low pressure system), followed by zonal flow (5.8%; 95% CI, 1.8-10%). The high-low pressure system and the northwesterly flow are associated with the lowest mortality. The seasonal patterns are consistent with the annual pattern. For mesoscale categories, in the cold period the highest mortality is observed during days characterized by the easterly flow category (increase 9.4%; 95% CI, 1.0-18.5% compared to flow without the main component). In the warm period, the highest mortality occurs during the strong southerly flow category (8.5% increase; 95% CI, 2.0-15.4% compared again to flow without the main component). Adjusting for ambient particle levels leaves the estimated associations unchanged for the synoptic categories and slightly increases the effects of mesoscale categories. In conclusion, synoptic and mesoscale weather classification is a useful tool for studying the weather-health associations in a warm Mediterranean climate situation. PMID:11445513

Attachment stability and the emergence of unresolved representations during adolescence.

PubMed

Aikins, Julie Wargo; Howes, Carollee; Hamilton, Claire

2009-09-01

This 15-year longitudinal study examined the stability of attachment representations from infancy to adolescence and investigated the emergence of unresolved representations during adolescence in a sample of 47 16-year-olds. Attachment was assessed at 12 months using the Strange Situation Procedure, at 4 years using the modified Strange Situation Procedure, and again at 16 years with the Adult Attachment Projective (AAP). The emergence of unresolved classifications in adolescence (AAP) was associated with higher rates of negative life events, low levels of early mother-child relationship security (an aggregate measure of the 12-month and 4-year measures), negative teacher-child relationship experiences in middle childhood, and low early adolescent friendship quality. The results support the growing body of evidence suggesting that changes in attachment are lawful, while adding to the growing understanding of the emergence of unresolved attachment representations.

Mixtures of macrophyte growth forms promote nitrogen cycling in wetlands.

PubMed

Choudhury, Maidul I; McKie, Brendan G; Hallin, Sara; Ecke, Frauke

2018-09-01

The importance of aquatic plant diversity in regulating nutrient cycling in wetlands remains poorly understood. We investigated how variation in macrophyte growth form (emerging, submerged and bryophyte) combinations and species mixtures affect nitrogen (N) removal from the water and N accumulation in plant biomass. We conducted a wetland mesocosm experiment for 100 days during July-September 2015. Twelve species were grown in mono- and in two-species mixed cultures for a total of 32 single and two-growth form combinations. Nitrogen removal from the water was quantified on three occasions during the experiment, while N accumulation in plant biomass was determined following termination of the experiment. The number of species and growth forms present increased N removal and accumulation. The growth form combinations of emerging and bryophyte species showed the highest N accumulation and N removal from water, followed by combinations of emerging species. By contrast, submerged species growing in the presence of emerging or other submerged species showed the lowest levels of N accumulation and N removal. Temporal variation in N removal also differed among growth form combinations: N removal was highest for emerging-bryophyte combinations in July, but peaked for the emerging-submerged and emerging-bryophyte combinations in August. Indeed, the occurrence of complementarity among macrophyte species, particularly in combinations of bryophyte and emerging species, enhanced N removal and uptake during the entire growing season. Our study highlights the importance of bryophytes, which have been neglected in research on nutrient cycling in wetlands, for aquatic N cycling, especially given their worldwide distribution across biomes. Overall, our findings point towards the potential important role of the diversity of macrophyte growth forms in regulating key ecosystem processes related to N cycling in wetlands. Copyright © 2018 Elsevier B.V. All rights reserved.

A Passive Badge Dosimeter for HCL Detection and Measurement - SBIR 90.I (A90-189)

DTIC Science & Technology

1990-10-02

Microencapsulation ; Toxic gas detection; Combustion Products; RA III; ’i6.PRICECOOE SORR OF____PAGOfABSRAC 17. SECURITY CLASSIFICATION It. SECURITY... microencapsulated samples, all of the sample? changed color when exposed to sufficiently high concentrations of Ha vapor. In general, detector sensitivity...correlted with indicator pKa with the highest sensitivity being noted for indicators with pKa- 7.0. The microencapsulated dye/liquid crystal droplets

ICA-Based Imagined Conceptual Words Classification on EEG Signals.

PubMed

Imani, Ehsan; Pourmohammad, Ali; Bagheri, Mahsa; Mobasheri, Vida

2017-01-01

Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator function, the classification accuracies were almost the same and not very different. Linear discriminant analysis (LDA) in comparison with the neural network yielded higher classification accuracies. ICA is a suitable algorithm for recognizing of the word's concept and its place in the brain. Achieved results from this experiment were the same compared with the results from other methods such as functional magnetic resonance imaging and methods based on the brain signals (EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy was obtained by extracting the target signal from the output of the ICA and extracting the features of coefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classification accuracy more than 60%.

Hair removal-related injuries in the United States, 1991-2014.

PubMed

Swain, Thomas A; Tully, Albert Scott; Redford, Travis; McGwin, Gerald

2016-12-01

Hair removal practices have changed in frequency and location on the body. Previous research on hair removal injuries has focused on a specific body region, age, or gender. This study sought to take a broader perspective of hair removal-associated injuries in the United States which sought treatment at emergency departments. Data from the National Electronic Injury Surveillance System (NEISS) from 1991 to 2014 were used to identify hair removal-related injuries. Incidence rates were determined for the overall population and stratified by gender and age category using US Census Bureau population estimates. From 1991 to 2014, there were an estimated 292 053 hair removal-associated injuries in the United States. The overall incidence rate was highest in 2013 (9/100 000). Those aged 65+ had the highest incidence from 1991 to 2010 with those aged 19-34 having the highest rate starting in 2011. When stratified by body region injured, males had highest injury rates to the face and females had highest rates to the lower limbs. Starting in 2010, those aged 19-34 had higher incidence particularly for pubic and trunk regions. The incidence of hair removal-associated injuries seen by emergency departments increased nearly ninefold between 1991 and 2013. Due to the increased incidence among 19- to 34-year-olds, caution should be taken particularly for this age group when undergoing depilatory practices. Overall, individuals should practice safe and acceptable usage of hair removal products to reduce the risk of injury. © 2016 Wiley Periodicals, Inc.

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

NASA Astrophysics Data System (ADS)

Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

2015-02-01

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

15 CFR 922.82 - Prohibited or otherwise regulated activities.

Code of Federal Regulations, 2014 CFR

2014-01-01

...., Endangered Species Act (ESA), as amended, 16 U.S.C. 1531 et seq., Migratory Bird Treaty Act, as amended... classification) that is approved in accordance with section 312 of the Federal Water Pollution Control Act, as..., except for the operation of motorized personal watercraft for emergency search and rescue missions or law...

15 CFR 922.82 - Prohibited or otherwise regulated activities.

Code of Federal Regulations, 2013 CFR

2013-01-01

...., Endangered Species Act (ESA), as amended, 16 U.S.C. 1531 et seq., Migratory Bird Treaty Act, as amended... classification) that is approved in accordance with section 312 of the Federal Water Pollution Control Act, as..., except for the operation of motorized personal watercraft for emergency search and rescue missions or law...

15 CFR 922.82 - Prohibited or otherwise regulated activities.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Protection Act, as amended, (MMPA), 16 U.S.C. 1361 et seq., Endangered Species Act (ESA), as amended, 16 U.S... classification) that is approved in accordance with section 312 of the Federal Water Pollution Control Act, as..., except for the operation of motorized personal watercraft for emergency search and rescue missions or law...

Comparing Two CBM Maze Selection Tools: Considering Scoring and Interpretive Metrics for Universal Screening

ERIC Educational Resources Information Center

Ford, Jeremy W.; Missall, Kristen N.; Hosp, John L.; Kuhle, Jennifer L.

2016-01-01

Advances in maze selection curriculum-based measurement have led to several published tools with technical information for interpretation (e.g., norms, benchmarks, cut-scores, classification accuracy) that have increased their usefulness for universal screening. A range of scoring practices have emerged for evaluating student performance on maze…

Treatment of Children with Speech Oral Placement Disorders (OPDs): A Paradigm Emerges

ERIC Educational Resources Information Center

Bahr, Diane; Rosenfeld-Johnson, Sara

2010-01-01

Epidemiological research was used to develop the Speech Disorders Classification System (SDCS). The SDCS is an important speech diagnostic paradigm in the field of speech-language pathology. This paradigm could be expanded and refined to also address treatment while meeting the standards of evidence-based practice. The article assists that process…

Using Similarities and Differences: A Meta-Analysis of Its Effects and Emergent Patterns

ERIC Educational Resources Information Center

Apthorp, Helen S.; Igel, Charles; Dean, Ceri

2012-01-01

The purpose of the study was to update previous meta-analytic findings on the effectiveness of using similarities and differences as an instructional strategy. The strategy includes facilitating student comparison, classification, use of analogies, and use of metaphors. Previously, Marzano, Pickering, and Pollock reported a mean effect size of…

Management of Status Epilepticus in Children

PubMed Central

Smith, Douglas M.; McGinnis, Emily L.; Walleigh, Diana J.; Abend, Nicholas S.

2016-01-01

Status epilepticus is a common pediatric neurological emergency. Management includes prompt administration of appropriately selected anti-seizure medications, identification and treatment of seizure precipitant(s), as well as identification and management of associated systemic complications. This review discusses the definitions, classification, epidemiology and management of status epilepticus and refractory status epilepticus in children. PMID:27089373

Structures-of-the-Whole: Is There Any Glue to Hold the Concrete-Operational "Stage" Together?

ERIC Educational Resources Information Center

Brainerd, Charles J.

Studies concerned with the synchronous emergence prediction of Piaget's structures-of-the-whole principle are discussed in conjunction with three groups of concrete-operational skills: (1) transitivity/conservation/class inclusion; (2) double classification/double seriation; and (3) ordinal, cardinal, and natural number concepts. Findings show…

The Microbial Rosetta Stone Database: A compilation of global and emerging infectious microorganisms and bioterrorist threat agents

PubMed Central

Ecker, David J; Sampath, Rangarajan; Willett, Paul; Wyatt, Jacqueline R; Samant, Vivek; Massire, Christian; Hall, Thomas A; Hari, Kumar; McNeil, John A; Büchen-Osmond, Cornelia; Budowle, Bruce

2005-01-01

Background Thousands of different microorganisms affect the health, safety, and economic stability of populations. Many different medical and governmental organizations have created lists of the pathogenic microorganisms relevant to their missions; however, the nomenclature for biological agents on these lists and pathogens described in the literature is inexact. This ambiguity can be a significant block to effective communication among the diverse communities that must deal with epidemics or bioterrorist attacks. Results We have developed a database known as the Microbial Rosetta Stone. The database relates microorganism names, taxonomic classifications, diseases, specific detection and treatment protocols, and relevant literature. The database structure facilitates linkage to public genomic databases. This paper focuses on the information in the database for pathogens that impact global public health, emerging infectious organisms, and bioterrorist threat agents. Conclusion The Microbial Rosetta Stone is available at . The database provides public access to up-to-date taxonomic classifications of organisms that cause human diseases, improves the consistency of nomenclature in disease reporting, and provides useful links between different public genomic and public health databases. PMID:15850481

Scheduling terminology for oral and maxillofacial surgery. Are we speaking a universal language?

PubMed

Howe, T E; Varley, I; Allen, J E; Glossop, A; McKechnie, A

2017-05-01

Use of a universal vocabulary to assist with the scheduling of operations has been shown to considerably reduce delays and improve the use of theatre resources. Within the UK the National Confidential Enquiry into Patient Outcome and Death (NCEPOD) has established a classification to assist with the triage of both emergency and non-emergency operating lists. We completed a survey to assess the uptake and understanding of this classification when scheduling maxillofacial operations. From a list of eight scheduling terms, respondents had to choose one each for 20 different clinical situations (that represented equally) immediate, urgent, expedited, and elective operations as defined by them. A total of 50 surveys were collated. Only 65% of answers selected represented NCPOD terms. 25% of answers represented a term higher and 18% a term lower, on the scale of intervention for the same category of situation. Current NCEPOD terms do not seem to be used universally and are poorly understood. Considerable variation in terminology exists when scheduling maxillofacial operations. Copyright © 2016 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Trends in Adult Cancer-Related Emergency Department Utilization: An Analysis of Data From the Nationwide Emergency Department Sample.

PubMed

Rivera, Donna R; Gallicchio, Lisa; Brown, Jeremy; Liu, Benmei; Kyriacou, Demetrios N; Shelburne, Nonniekaye

2017-10-12

The emergency department (ED) is used to manage cancer-related complications among the 15.5 million people living with cancer in the United States. However, ED utilization patterns by the population of US adults with cancer have not been previously evaluated or described in published literature. To estimate the proportion of US ED visits made by adults with a cancer diagnosis, understand the clinical presentation of adult patients with cancer in the ED, and examine factors related to inpatient admission within this population. Nationally representative data comprised of 7 survey cycles (January 2006-December 2012) from the Nationwide Emergency Department Sample were analyzed. Identification of adult (age ≥18 years) cancer-related visits was based on Clinical Classifications Software diagnoses documented during the ED visit. Weighted frequencies and proportions of ED visits among adult patients with cancer by demographic, geographic, and clinical characteristics were calculated. Weighted multivariable logistic regression was used to examine the associations between inpatient admission and key demographic and clinical variables for adult cancer-related ED visits. Adult cancer-related ED utilization patterns; identification of primary reason for ED visit; patient-related factors associated with inpatient admission from the ED. Among an estimated 696 million weighted adult ED visits from January 2006 to December 2012, 29.5 million (4.2%) were made by a patient with a cancer diagnosis. The most common cancers associated with an ED visit were breast, prostate, and lung cancer, and most common primary reasons for visit were pneumonia (4.5%), nonspecific chest pain (3.7%), and urinary tract infection (3.2%). Adult cancer-related ED visits resulted in inpatient admissions more frequently (59.7%) than non-cancer-related visits (16.3%) (P < .001). Septicemia (odds ratio [OR], 91.2; 95% CI, 81.2-102.3) and intestinal obstruction (OR, 10.94; 95% CI, 10.6-11.4) were associated with the highest odds of inpatient admission. Consistent with national prevalence statistics among adults, breast, prostate, and lung cancer were the most common cancer diagnoses presenting to the ED. Pneumonia was the most common reason for adult cancer-related ED visits with an associated high inpatient admission rate. This analysis highlights cancer-specific ED clinical presentations and the opportunity to inform patient and system-directed prevention and management strategies.

AVHRR channel selection for land cover classification

USGS Publications Warehouse

Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

2002-01-01

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.

Investigation of hydrometeor classification uncertainties through the POLARRIS polarimetric radar simulator

NASA Astrophysics Data System (ADS)

Dolan, B.; Rutledge, S. A.; Barnum, J. I.; Matsui, T.; Tao, W. K.; Iguchi, T.

2017-12-01

POLarimetric Radar Retrieval and Instrument Simulator (POLARRIS) is a framework that has been developed to simulate radar observations from cloud resolving model (CRM) output and subject model data and observations to the same retrievals, analysis and visualization. This framework not only enables validation of bulk microphysical model simulated properties, but also offers an opportunity to study the uncertainties associated with retrievals such as hydrometeor classification (HID). For the CSU HID, membership beta functions (MBFs) are built using a set of simulations with realistic microphysical assumptions about axis ratio, density, canting angles, size distributions for each of ten hydrometeor species. These assumptions are tested using POLARRIS to understand their influence on the resulting simulated polarimetric data and final HID classification. Several of these parameters (density, size distributions) are set by the model microphysics, and therefore the specific assumptions of axis ratio and canting angle are carefully studied. Through these sensitivity studies, we hope to be able to provide uncertainties in retrieved polarimetric variables and HID as applied to CRM output. HID retrievals assign a classification to each point by determining the highest score, thereby identifying the dominant hydrometeor type within a volume. However, in nature, there is rarely just one a single hydrometeor type at a particular point. Models allow for mixing ratios of different hydrometeors within a grid point. We use the mixing ratios from CRM output in concert with the HID scores and classifications to understand how the HID algorithm can provide information about mixtures within a volume, as well as calculate a confidence in the classifications. We leverage the POLARRIS framework to additionally probe radar wavelength differences toward the possibility of a multi-wavelength HID which could utilize the strengths of different wavelengths to improve HID classifications. With these uncertainties and algorithm improvements, cases of convection are studied in a continental (Oklahoma) and maritime (Darwin, Australia) regime. Observations from C-band polarimetric data in both locations are compared to CRM simulations from NU-WRF using the POLARRIS framework.

Integrating Multibeam Backscatter Angular Response, Mosaic and Bathymetry Data for Benthic Habitat Mapping

PubMed Central

Che Hasan, Rozaimi; Ierodiaconou, Daniel; Laurenson, Laurie; Schimel, Alexandre

2014-01-01

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management. PMID:24824155

Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium.

PubMed

Mitry, Danny; Peto, Tunde; Hayat, Shabina; Morgan, James E; Khaw, Kay-Tee; Foster, Paul J

2013-01-01

Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥ 96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

PubMed

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P<0.05). When applying Qual1=Color pattern 1 for downgrading and Qual1=Color pattern 5 for upgrading the BI-RADS categories, we obtained the highest Az value (0.951), and achieved a significantly higher specificity (86.56%, P=0.002) than that of the US (81.18%) with the same sensitivity (94.96%). The qualitative classification proposed in this study may be representative of SWE parameters and has potential to be relevant assistance in breast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

External Validation of the European Hernia Society Classification for Postoperative Complications after Incisional Hernia Repair: A Cohort Study of 2,191 Patients.

PubMed

Kroese, Leonard F; Kleinrensink, Gert-Jan; Lange, Johan F; Gillion, Jean-Francois

2018-03-01

Incisional hernia is a frequent complication after midline laparotomy. Surgical hernia repair is associated with complications, but no clear predictive risk factors have been identified. The European Hernia Society (EHS) classification offers a structured framework to describe hernias and to analyze postoperative complications. Because of its structured nature, it might prove to be useful for preoperative patient or treatment classification. The objective of this study was to investigate the EHS classification as a predictor for postoperative complications after incisional hernia surgery. An analysis was performed using a registry-based, large-scale, prospective cohort study, including all patients undergoing incisional hernia surgery between September 1, 2011 and February 29, 2016. Univariate analyses and multivariable logistic regression analysis were performed to identify risk factors for postoperative complications. A total of 2,191 patients were included, of whom 323 (15%) had 1 or more complications. Factors associated with complications in univariate analyses (p < 0.20) and clinically relevant factors were included in the multivariable analysis. In the multivariable analysis, EHS width class, incarceration, open surgery, duration of surgery, Altemeier wound class, and therapeutic antibiotic treatment were independent risk factors for postoperative complications. Third recurrence and emergency surgery were associated with fewer complications. Incisional hernia repair is associated with a 15% complication rate. The EHS width classification is associated with postoperative complications. To identify patients at risk for complications, the EHS classification is useful. Copyright © 2017. Published by Elsevier Inc.

Validation of the new diagnosis grouping system for pediatric emergency department visits using the International Classification of Diseases, 10th Revision.

PubMed

Lee, Jin Hee; Hong, Ki Jeong; Kim, Do Kyun; Kwak, Young Ho; Jang, Hye Young; Kim, Hahn Bom; Noh, Hyun; Park, Jungho; Song, Bongkyu; Jung, Jae Yun

2013-12-01

A clinically sensible diagnosis grouping system (DGS) is needed for describing pediatric emergency diagnoses for research, medical resource preparedness, and making national policy for pediatric emergency medical care. The Pediatric Emergency Care Applied Research Network (PECARN) developed the DGS successfully. We developed the modified PECARN DGS based on the different pediatric population of South Korea and validated the system to obtain the accurate and comparable epidemiologic data of pediatric emergent conditions of the selected population. The data source used to develop and validate the modified PECARN DGS was the National Emergency Department Information System of South Korea, which was coded by the International Classification of Diseases, 10th Revision (ICD-10) code system. To develop the modified DGS based on ICD-10 code, we matched the selected ICD-10 codes with those of the PECARN DGS by the General Equivalence Mappings (GEMs). After converting ICD-10 codes to ICD-9 codes by GEMs, we matched ICD-9 codes into PECARN DGS categories using the matrix developed by PECARN group. Lastly, we conducted the expert panel survey using Delphi method for the remaining diagnosis codes that were not matched. A total of 1879 ICD-10 codes were used in development of the modified DGS. After 1078 (57.4%) of 1879 ICD-10 codes were assigned to the modified DGS by GEM and PECARN conversion tools, investigators assigned each of the remaining 801 codes (42.6%) to DGS subgroups by 2 rounds of electronic Delphi surveys. And we assigned the remaining 29 codes (4%) into the modified DGS at the second expert consensus meeting. The modified DGS accounts for 98.7% and 95.2% of diagnoses of the 2008 and 2009 National Emergency Department Information System data set. This modified DGS also exhibited strong construct validity using the concepts of age, sex, site of care, and seasons. This also reflected the 2009 outbreak of H1N1 influenza in Korea. We developed and validated clinically feasible and sensible DGS system for describing pediatric emergent conditions in Korea. The modified PECARN DGS showed good comprehensiveness and demonstrated reliable construct validity. This modified DGS based on PECARN DGS framework may be effectively implemented for research, reporting, and resource planning in pediatric emergency system of South Korea.

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

PubMed Central

Jiang, Hao; Zhao, Dehua; Cai, Ying; An, Shuqing

2012-01-01

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.

Effect of harmless acute pancreatitis score, red cell distribution width and neutrophil/lymphocyte ratio on the mortality of patients with nontraumatic acute pancreatitis at the emergency department

PubMed Central

Gülen, Bedia; Sonmez, Ertan; Yaylaci, Serpil; Serinken, Mustafa; Eken, Cenker; Dur, Ali; Turkdogan, Figen Tunali; Söğüt, Özgür

2015-01-01

BACKGROUND: Harmless acute pancreatitis score (HAPS), neutrophile/lymphocyte ratio and red blood cell distribution width (RDW) are used to determine the early prognosis of patients diagnosed with nontraumatic acute pancreatitis in the emergency department (ED). METHODS: Patients diagnosed with acute pancreatitis (K 85.9) in the ED according to the ICD10 coding during one year were included in the study. Patients with chronic pancreatitis and those who had missing data in their files were excluded from the study. Patients who did not have computed tomography (CT) in the ED were not included in the study. RESULTS: Ultimately, 322 patients were included in the study. The median age of the patients was 53.1 (IQR=36–64). Of the patients, 68.1% (n=226) had etiological causes of the biliary tract. The mortality rate of these patients within the first 48 hours was 4.3% (n=14). In the logistic regression analysis performed by using Balthazar classification, HAPS score, RDW, neutrophile/lymphocyte ratio, age, diabetes mellitus and systolic blood pressure, the only independent variable in determining mortality was assigned as Balthazar classification (OR: 15; 95% CI: 3.5 to 64.4). CONCLUSIONS: HAPS, neutrophile/lymphocyte ratio and RDW were not effective in determining the mortality of nontraumatic acute pancreatitis cases within the first 48 hours. The only independent variable for determining the mortality was Balthazar classification. PMID:25802563

Examining transgender health through the International Classification of Functioning, Disability, and Health's (ICF) Contextual Factors.

PubMed

Jacob, Melissa; Cox, Steven R

2017-12-01

For many transgender individuals, medical intervention is necessary to live as their desired gender. However, little is known about Contextual Factors (i.e., Environmental and Personal) that may act as facilitators and barriers in the health of transgender individuals. Therefore, this paper sought to examine Contextual Factors of the World Health Organization's International Classification of Functioning, Disability, and Health that may facilitate or negatively impact the physical, psychological, and social functioning of transgender individuals. A literature review was conducted to identify Environmental and Personal Factors that may influence transgender individuals' physical, psychological, and social functioning. Seven electronic databases were searched. In total, 154 records were reviewed, and 41 articles and other records met inclusion criteria. Three general themes emerged for Environmental Factors: family and social networks, education, and health care. Three general themes also emerged for Personal Factors: socioeconomic status, race, and age. Transgender individuals benefit from gender-affirming services, improved family and social support systems, and competent provider care. Educational training programs, including medical curricula or workshops, might provide the greatest benefit in improving transgender health by increasing the knowledge and cultural competency of health professionals working with this population. Given the diversity of gender expression, differences in lived experiences, and potential for enduring persistent "double discrimination" due to the intersectional relationships between socioeconomic status, race, and/or age, health professionals must approach transgender health using a holistic lens such as the World Health Organization's International Classification of Functioning, Disability, and Health.

Effect of harmless acute pancreatitis score, red cell distribution width and neutrophil/lymphocyte ratio on the mortality of patients with nontraumatic acute pancreatitis at the emergency department.

PubMed

Gülen, Bedia; Sonmez, Ertan; Yaylaci, Serpil; Serinken, Mustafa; Eken, Cenker; Dur, Ali; Turkdogan, Figen Tunali; Söğüt, Özgür

2015-01-01

Harmless acute pancreatitis score (HAPS), neutrophile/lymphocyte ratio and red blood cell distribution width (RDW) are used to determine the early prognosis of patients diagnosed with nontraumatic acute pancreatitis in the emergency department (ED). Patients diagnosed with acute pancreatitis (K 85.9) in the ED according to the ICD10 coding during one year were included in the study. Patients with chronic pancreatitis and those who had missing data in their files were excluded from the study. Patients who did not have computed tomography (CT) in the ED were not included in the study. Ultimately, 322 patients were included in the study. The median age of the patients was 53.1 (IQR=36-64). Of the patients, 68.1% (n=226) had etiological causes of the biliary tract. The mortality rate of these patients within the first 48 hours was 4.3% (n=14). In the logistic regression analysis performed by using Balthazar classification, HAPS score, RDW, neutrophile/lymphocyte ratio, age, diabetes mellitus and systolic blood pressure, the only independent variable in determining mortality was assigned as Balthazar classification (OR: 15; 95% CI: 3.5 to 64.4). HAPS, neutrophile/lymphocyte ratio and RDW were not effective in determining the mortality of nontraumatic acute pancreatitis cases within the first 48 hours. The only independent variable for determining the mortality was Balthazar classification.

Solar Flare Occurrence Rate and Probability in Terms of the Sunspot Classification Supplemented with Sunspot Area and Its Changes

NASA Astrophysics Data System (ADS)

Lee, K.; Moon, Y.; Lee, J.; Na, H.; Lee, K.

2013-12-01

We investigate the solar flare occurrence rate and daily flare probability in terms of the sunspot classification supplemented with sunspot area and its changes. For this we use the NOAA active region data and GOES solar flare data for 15 years (from January 1996 to December 2010). We consider the most flare-productive 11 sunspot classes in the McIntosh sunspot group classification. Sunspot area and its changes can be a proxy of magnetic flux and its emergence/cancellation, respectively. We classify each sunspot group into two sub-groups by its area: 'Large' and 'Small'. In addition, for each group, we classify it into three sub-groups according to sunspot area changes: 'Decrease', 'Steady', and 'Increase'. As a result, in the case of compact groups, their flare occurrence rates and daily flare probabilities noticeably increase with sunspot group area. We also find that the flare occurrence rates and daily flare probabilities for the 'Increase' sub-groups are noticeably higher than those for the other sub-groups. In case of the (M + X)-class flares in the ';Dkc' group, the flare occurrence rate of the 'Increase' sub-group is three times higher than that of the 'Steady' sub-group. The mean flare occurrence rates and flare probabilities for all sunspot groups increase with the following order: 'Decrease', 'Steady', and 'Increase'. Our results statistically demonstrate that magnetic flux and its emergence enhance the occurrence of major solar flares.

Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature.

PubMed

Holmes, John H; Elliott, Thomas E; Brown, Jeffrey S; Raebel, Marsha A; Davidson, Arthur; Nelson, Andrew F; Chung, Annie; La Chance, Pierre; Steiner, John F

2014-01-01

To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). Medline, PubMed, EMBASE, CINAHL, and INSPEC were searched for relevant documents published through July 31, 2013 using a systematic approach. Only documents relating to DRNs in the USA were included. Documents were analyzed using a classification framework consisting of 10 facets to identify themes. 6641 documents were retrieved. After screening for duplicates and relevance, 38 were included in the final review. A peer-reviewed literature on data warehouse governance is emerging, but is still sparse. Peer-reviewed publications on UK research network governance were more prevalent, although not reviewed for this analysis. All 10 classification facets were used, with some documents falling into two or more classifications. No document addressed costs associated with governance. Even though DRNs are emerging as vehicles for research and public health surveillance, understanding of DRN data governance policies and procedures is limited. This is expected to change as more DRN projects disseminate their governance approaches as publicly available toolkits and peer-reviewed publications. While peer-reviewed, US-based DRN data warehouse governance publications have increased, DRN developers and administrators are encouraged to publish information about these programs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Seed germination ecology of feather lovegrass [Eragrostis tenella (L.) Beauv. Ex Roemer & J.A. Schultes].

PubMed

Chauhan, Bhagirath S

2013-01-01

Feather lovegrass [Eragrostis tenella (L.) Beauv. Ex Roemer & J.A. Schultes] is a C4 grass weed that has the ability to grow in both lowland and upland conditions. Experiments were conducted in the laboratory and screenhouse to evaluate the effect of environmental factors on germination, emergence, and growth of this weed species. Germination in the light/dark regime was higher at alternating day/night temperatures of 30/20 °C (98%) than at 35/25 °C (83%) or 25/15 °C (62%). Germination was completely inhibited by darkness. The osmotic potential and sodium chloride concentrations required for 50% inhibition of maximum germination were -0.7 MPa and 76 mM, respectively. The highest seedling emergence (69%) was observed from the seeds sown on the soil surface and no seedlings emerged from seeds buried at depths of 0.5 cm or more. The use of residue as mulches significantly reduced the emergence and biomass of feather lovegrass seedlings. A residue amount of 0.5 t ha(-1) was needed to suppress 50% of the maximum seedlings. Because germination was strongly stimulated by light and seedling emergence was the highest for the seeds sown on the soil surface, feather lovegrass is likely to become a problematic weed in zero-till systems. The knowledge gained from this study could help in developing effective and sustainable weed management strategies.

Characterizing emergency admissions of patients with sickle cell crisis in NHS brent: observational study

PubMed Central

Green, Stuart A; AlJuburi, Ghida; Majeed, Azeem; Okoye, Ogo; Amobi, Carole; Banarsee, Ricky; Phekoo, Karen J

2012-01-01

Objectives To characterize emergency admissions for patients with sickle cell crisis in NHS Brent and to determine which patients and practices may benefit most from primary care intervention. Design Observational study Setting Emergency departments attended by residents of the London borough of Brent Participants Patients with sickle cell disease registered with a general practitioner (GP) in the borough of Brent Main outcome measures Analysis of admissions between January 2008 and July 2010 that included length of stay (average and <2 days versus ≥2 days) by age group and registered GP practice. Results Thirty six percent of sickle cell disease admission spells resulted in a length of stay of less than two days. Seventy four percent of total bed days are associated with patients with more than one admission during the period of analysis, i.e. multiple admissions. Two general practices in Brent were identified as having the highest number of patients admitted to the emergency department for sickle cell crisis and may benefit most from primary care intervention. Discussion Patients with short length of stay and multiple admissions may be potentially amenable to primary care intervention. The practices which have the highest numbers of sickle cell disease patients who frequently seek emergency care will be earmarked for an education intervention designed to help further engage general practitioners in the care and management of their sickle cell patients. PMID:22768371

Characterizing emergency admissions of patients with sickle cell crisis in NHS brent: observational study.

PubMed

Green, Stuart A; Aljuburi, Ghida; Majeed, Azeem; Okoye, Ogo; Amobi, Carole; Banarsee, Ricky; Phekoo, Karen J

2012-06-01

To characterize emergency admissions for patients with sickle cell crisis in NHS Brent and to determine which patients and practices may benefit most from primary care intervention. Observational study Emergency departments attended by residents of the London borough of Brent Patients with sickle cell disease registered with a general practitioner (GP) in the borough of Brent Analysis of admissions between January 2008 and July 2010 that included length of stay (average and <2 days versus ≥2 days) by age group and registered GP practice. Thirty six percent of sickle cell disease admission spells resulted in a length of stay of less than two days. Seventy four percent of total bed days are associated with patients with more than one admission during the period of analysis, i.e. multiple admissions. Two general practices in Brent were identified as having the highest number of patients admitted to the emergency department for sickle cell crisis and may benefit most from primary care intervention. Patients with short length of stay and multiple admissions may be potentially amenable to primary care intervention. The practices which have the highest numbers of sickle cell disease patients who frequently seek emergency care will be earmarked for an education intervention designed to help further engage general practitioners in the care and management of their sickle cell patients.

ABCD classification system: a novel classification for subaxial cervical spine injuries.

PubMed

Shousha, Mootaz

2014-04-20

The classification system was derived through a retrospective analysis of 73 consecutive cases of subaxial cervical spine injury as well as thorough literature review. To define a new classification system for subaxial cervical spine injuries. There exist several methods to classify subaxial cervical spine injuries but no single system has emerged as clearly superior to the others. On the basis of a 2-column anatomical model, the first part of the proposed classification is an anatomical description of the injury. It delivers the information whether the injury is bony, ligamentous, or a combined one. The first 4 alphabetical letters have been used for simplicity. Each column is represented by an alphabetical letter from A to D. Each letter has a radiological meaning (A = Absent injury, B = Bony lesion, C = Combined bony and ligamentous, D = Disc or ligamentous injury).The second part of the classification is represented by 3 modifiers. These are the neurological status of the patient (N), the degree of spinal canal stenosis (S), and the degree of instability (I). For simplicity, each modifier was graded in an ascending pattern of severity from zero to 2. The last part is optional and denotes which radiological examination has been used to define the injury type. The new ABCD classification was applicable for all patients. The most common type was anterior ligamentous and posterior combined injury "DC" (37.9%), followed by "DD" injury in 12% of the cases. Through this work a new classification for cervical spine injuries is proposed. The aim is to establish criteria for a common language in description of cervical injuries aiming for simplification, especially for junior residents. Each letter and each sign has a meaning to deliver the largest amount of information. Both the radiological as well as the clinical data are represented in this scheme. However, further evaluation of this classification is needed. 3.

Prescription of opioid analgesics for nontraumatic dental conditions in emergency departments.

PubMed

Okunseri, Christopher; Dionne, Raymond A; Gordon, Sharon M; Okunseri, Elaye; Szabo, Aniko

2015-11-01

Opioid analgesics prescribed for nontraumatic dental conditions (NTDCs) by emergency physicians continue to receive attention because of the associated potential for misuse, abuse and addiction. This study examined rates of prescription of opioid analgesics and types of opioid analgesics prescribed for NTDC visits in U.S. emergency departments. Data from the National Hospital Ambulatory Medical Care Survey from 2007 to 2010 were analyzed. Descriptive statistics and logistic regression analysis were performed and adjusted for the survey design. NTDCs made up 1.7% of all ED visits from 2007 to 2010. The prescription of opioid analgesics was 50.3% for NTDC and 14.8% for non-NTDC visits. The overall rate of opioid analgesics prescribed for NTDCs remained fairly stable from 2007 through 2010. Prescription of opioids was highest among patients aged 19-33 years (56.8%), self-paying (57.1%), and non-Hispanic Whites (53.2%). The probability of being prescribed hydrocodone was highest among uninsured patients (68.7%) and for oxycodone, it was highest among private insurance patients (33.6%). Compared to 34-52 year olds, children 0-4 years were significantly more likely to be prescribed codeine and less likely to be prescribed oxycodone. Compared to non-Hispanic Whites, non-Hispanic Blacks had significantly higher odds of been prescribed codeine and somewhat lower odds of been prescribed oxycodone, but it was not statistically significant. There was no significant change in the rates of opioid analgesics prescribed over time for NTDC visits to EDs. Age, payer type and race/ethnicity were significant predictors for the prescription of different opioid analgesics by emergency physicians for NTDC visits. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Influence of Urbanicity and County Characteristics on the Association between Ozone and Asthma Emergency Department Visits in North Carolina

PubMed Central

Rappold, Ana G.; Davis, J. Allen; Richardson, David B.; Waller, Anna E.; Luben, Thomas J.

2014-01-01

Background: Air pollution epidemiologic studies, often conducted in large metropolitan areas because of proximity to regulatory monitors, are limited in their ability to examine potential associations between air pollution exposures and health effects in rural locations. Methods: Using a time-stratified case-crossover framework, we examined associations between asthma emergency department (ED) visits in North Carolina (2006–2008), collected by a surveillance system, and short-term ozone (O3) exposures using predicted concentrations from the Community Multiscale Air Quality (CMAQ) model. We estimated associations by county groupings based on four urbanicity classifications (representative of county size and urban proximity) and county health. Results: O3 was associated with asthma ED visits in all-year and warm season (April–October) analyses [odds ratio (OR) = 1.019; 95% CI: 0.998, 1.040; OR = 1.020; 95% CI: 0.997, 1.044, respectively, for a 20-ppb increase in lag 0–2 days O3]. The association was strongest in Less Urbanized counties, with no evidence of a positive association in Rural counties. Associations were similar when adjusted for fine particulate matter in copollutant models. Associations were stronger for children (5–17 years of age) compared with other age groups, and for individuals living in counties identified with poorer health status compared with counties that had the highest health rankings, although estimated associations for these subgroups had larger uncertainty. Conclusions: Associations between short-term O3 exposures and asthma ED visits differed by overall county health and urbanicity, with stronger associations in Less Urbanized counties, and no positive association in Rural counties. Results also suggest that children are at increased risk of O3-related respiratory effects. Citation: Sacks JD, Rappold AG, Davis JA Jr, Richardson DB, Waller AE, Luben TJ. 2014. Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina. Environ Health Perspect 122:506–512; http://dx.doi.org/10.1289/ehp.1306940 PMID:24569869

Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

NASA Astrophysics Data System (ADS)

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

Challenges in the automated classification of variable stars in large databases

NASA Astrophysics Data System (ADS)

Graham, Matthew; Drake, Andrew; Djorgovski, S. G.; Mahabal, Ashish; Donalek, Ciro

2017-09-01

With ever-increasing numbers of astrophysical transient surveys, new facilities and archives of astronomical time series, time domain astronomy is emerging as a mainstream discipline. However, the sheer volume of data alone - hundreds of observations for hundreds of millions of sources - necessitates advanced statistical and machine learning methodologies for scientific discovery: characterization, categorization, and classification. Whilst these techniques are slowly entering the astronomer's toolkit, their application to astronomical problems is not without its issues. In this paper, we will review some of the challenges posed by trying to identify variable stars in large data collections, including appropriate feature representations, dealing with uncertainties, establishing ground truths, and simple discrete classes.

Self-adjoint realisations of the Dirac-Coulomb Hamiltonian for heavy nuclei

NASA Astrophysics Data System (ADS)

Gallone, Matteo; Michelangeli, Alessandro

2018-02-01

We derive a classification of the self-adjoint extensions of the three-dimensional Dirac-Coulomb operator in the critical regime of the Coulomb coupling. Our approach is solely based upon the Kreĭn-Višik-Birman extension scheme, or also on Grubb's universal classification theory, as opposite to previous works within the standard von Neumann framework. This let the boundary condition of self-adjointness emerge, neatly and intrinsically, as a multiplicative constraint between regular and singular part of the functions in the domain of the extension, the multiplicative constant giving also immediate information on the invertibility property and on the resolvent and spectral gap of the extension.

History of tachinid classification (Diptera, Tachinidae)

PubMed Central

O’Hara, James E.

2013-01-01

Abstract The history of the classification of the Tachinidae (Diptera) is traced from Meigen to the present. The contributions of Robineau-Desvoidy, Townsend, Villeneuve, Mesnil, Herting, Wood and many others are discussed within a chronological, taxonomic, and geographic context. The gradual development of the Tachinidae into its modern concept as a family of the Oestroidea and the emergence of the classificatory scheme of tribes and subfamilies in use today are reviewed. Certain taxa that have in the past been difficult to place, or continue to be of uncertain affinity, are considered and some are given in a table to show their varied historical treatments. The more significant systematic works published on the Tachinidae in recent decades are enumerated chronologically. PMID:23878512

Using social media for disaster emergency management

NASA Astrophysics Data System (ADS)

Wang, Y. D.; Wang, T.; Ye, X. Y.; Zhu, J. Q.; Lee, J.

2016-06-01

Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011). In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?

PubMed

Bozkurt, Selen; Bostanci, Asli; Turhan, Murat

2017-08-11

The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination. In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used. Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model. Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.

London 2012 Paralympic swimming: passive drag and the classification system.

PubMed

Oh, Yim-Taek; Burkett, Brendan; Osborough, Conor; Formosa, Danielle; Payton, Carl

2013-09-01

The key difference between the Olympic and Paralympic Games is the use of classification systems within Paralympic sports to provide a fair competition for athletes with a range of physical disabilities. In 2009, the International Paralympic Committee mandated the development of new, evidence-based classification systems. This study aims to assess objectively the swimming classification system by determining the relationship between passive drag and level of swimming-specific impairment, as defined by the current swimming class. Data were collected on participants at the London 2012 Paralympic Games. The passive drag force of 113 swimmers (classes 3-14) was measured using an electro-mechanical towing device and load cell. Swimmers were towed on the surface of a swimming pool at 1.5 m/s while holding their most streamlined position. Passive drag ranged from 24.9 to 82.8 N; the normalised drag (drag/mass) ranged from 0.45 to 1.86 N/kg. Significant negative associations were found between drag and the swimming class (τ = -0.41, p < 0.01) and normalised drag and the swimming class (τ = -0.60, p < 0.01). The mean difference in drag between adjacent classes was inconsistent, ranging from 0 N (6 vs 7) to 11.9 N (5 vs 6). Reciprocal Ponderal Index (a measure of slenderness) correlated moderately with normalised drag (r(P) = -0.40, p < 0.01). Although swimmers with the lowest swimming class experienced the highest passive drag and vice versa, the inconsistent difference in mean passive drag between adjacent classes indicates that the current classification system does not always differentiate clearly between swimming groups.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.