Handling Imbalanced Data Sets in Multistage Classification

NASA Astrophysics Data System (ADS)

López, M.

Multistage classification is a logical approach, based on a divide-and-conquer solution, for dealing with problems with a high number of classes. The classification problem is divided into several sequential steps, each one associated to a single classifier that works with subgroups of the original classes. In each level, the current set of classes is split into smaller subgroups of classes until they (the subgroups) are composed of only one class. The resulting chain of classifiers can be represented as a tree, which (1) simplifies the classification process by using fewer categories in each classifier and (2) makes it possible to combine several algorithms or use different attributes in each stage. Most of the classification algorithms can be biased in the sense of selecting the most populated class in overlapping areas of the input space. This can degrade a multistage classifier performance if the training set sample frequencies do not reflect the real prevalence in the population. Several techniques such as applying prior probabilities, assigning weights to the classes, or replicating instances have been developed to overcome this handicap. Most of them are designed for two-class (accept-reject) problems. In this article, we evaluate several of these techniques as applied to multistage classification and analyze how they can be useful for astronomy. We compare the results obtained by classifying a data set based on Hipparcos with and without these methods.

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false DRG classification and weighting factors. 412.60... Determining Prospective Payment Federal Rates for Inpatient Operating Costs § 412.60 DRG classification and weighting factors. (a) Diagnosis-related groups. CMS establishs a classification of inpatient hospital...

Decomposition of body mass growth into linear and ponderal growth in children with application to India.

PubMed

Chaurasia, Aalok R

2017-02-01

In this paper, we decompose the difference between the weight of a child and the weight of a reference child into the difference between the height of the child and the height of the reference child and the difference between the weight per unit height of the child and the weight per unit height of the reference child. The decomposition provides the theoretical justification to the classification of the nutritional status proposed by Svedberg and by Nandy et al. An application of the decomposition framework to the Indian data shows that the level, depth and severity of the faltering of the growth of the body mass in Indian children are primarily due to the level, depth and severity of the faltering of the ponderal growth.

Mild, moderate, meaningful? Examining the psychological and functioning correlates of DSM-5 eating disorder severity specifiers.

PubMed

Gianini, Loren; Roberto, Christina A; Attia, Evelyn; Walsh, B Timothy; Thomas, Jennifer J; Eddy, Kamryn T; Grilo, Carlos M; Weigel, Thomas; Sysko, Robyn

2017-08-01

This study evaluated the DSM-5 severity specifiers for treatment-seeking groups of participants with anorexia nervosa (AN), the purging form of bulimia nervosa (BN), and binge-eating disorder (BED). Hundred and sixty-two participants with AN, 93 participants with BN, and 343 participants with BED were diagnosed using semi-structured interviews, sub-categorized using DSM-5 severity specifiers and compared on demographic and cross-sectional clinical measures. In AN, the number of previous hospitalizations and the duration of illness increased with severity, but there was no difference across severity groups on measures of eating pathology, depression, or measures of self-reported physical or emotional functioning. In BN, the level of eating concerns increased across the severity groups, but the groups did not differ on measures of depression, self-esteem, and most eating pathology variables. In BN, support was also found for an alternative severity classification scheme based upon number of methods of purging. In BED, levels of several measures of eating pathology and self-reported physical and emotional functioning increased across the severity groups. For BED, however, support was also found for an alternative severity classification scheme based upon overvaluation of shape and weight. Preliminary evidence was also found for a transdiagnostic severity index based upon overvaluation of shape and weight. Overall, these data show limited support for the DSM-5 severity specifiers for BN and modest support for the DSM-5 severity specifiers for AN and BED. © 2017 Wiley Periodicals, Inc.

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

PubMed

Shao, Shi-Yun; Shen, Kai-Quan; Ong, Chong Jin; Wilder-Smith, Einar P V; Li, Xiao-Ping

2009-02-01

An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.

A supervised learning rule for classification of spatiotemporal spike patterns.

PubMed

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as basis of diagnostic variability

PubMed Central

Campbell, J. Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D.; Hutcheson, Kelly; Shapiro, Michael J.; Repka, Michael X.; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E.; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing Expert classification of images of plus disease in ROP. Main Outcome Measures Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0 – 0.75, mean 0.30). RSD agreement ranged from 80 – 94% agreement for the dataset of 100 images, and 29 – 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R2=0.82 for dataset A, and P< 0.05 and adjusted R2 =0.6615 for dataset B). Conclusions and Relevance There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different “cut-points” for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future. PMID:27591053

Local classifier weighting by quadratic programming.

PubMed

Cevikalp, Hakan; Polikar, Robi

2008-10-01

It has been widely accepted that the classification accuracy can be improved by combining outputs of multiple classifiers. However, how to combine multiple classifiers with various (potentially conflicting) decisions is still an open problem. A rich collection of classifier combination procedures -- many of which are heuristic in nature -- have been developed for this goal. In this brief, we describe a dynamic approach to combine classifiers that have expertise in different regions of the input space. To this end, we use local classifier accuracy estimates to weight classifier outputs. Specifically, we estimate local recognition accuracies of classifiers near a query sample by utilizing its nearest neighbors, and then use these estimates to find the best weights of classifiers to label the query. The problem is formulated as a convex quadratic optimization problem, which returns optimal nonnegative classifier weights with respect to the chosen objective function, and the weights ensure that locally most accurate classifiers are weighted more heavily for labeling the query sample. Experimental results on several data sets indicate that the proposed weighting scheme outperforms other popular classifier combination schemes, particularly on problems with complex decision boundaries. Hence, the results indicate that local classification-accuracy-based combination techniques are well suited for decision making when the classifiers are trained by focusing on different regions of the input space.

Classification and valuation of postoperative complications in a randomized trial of open versus laparoscopic ventral herniorrhaphy.

PubMed

Kaafarani, H M A; Hur, K; Campasano, M; Reda, D J; Itani, K M F

2010-06-01

Generic instruments used for the valuation of health states (e.g., EuroQol) often lack sensitivity to notable differences that are relevant to particular diseases or interventions. We developed a valuation methodology specifically for complications following ventral incisional herniorrhaphy (VIH). Between 2004 and 2006, 146 patients were prospectively randomized to undergo laparoscopic (n = 73) or open (n = 73) VIH. The primary outcome of the trial was complications at 8 weeks. A three-step methodology was used to assign severity weights to complications. First, each complication was graded using the Clavien classification. Second, five reviewers were asked to independently and directly rate their perception of the severity of each class using a non-categorized visual analog scale. Zero represented an uncomplicated postoperative course, while 100 represented postoperative death. Third, the median, lowest, and highest values assigned to each class of complications were used to derive weighted complication scores for open and laparoscopic VIH. Open VIH had more complications than laparoscopic VIH (47.9 vs. 31.5%, respectively; P = 0.026). However, complications of laparoscopic VIH were more severe than those of open VIH. Non-parametric analysis revealed a statistically higher weighted complication score for open VIH (interquartile range: 0-20 for open vs. 0-10 for laparoscopic; P = 0.049). In the sensitivity analysis, similar results were obtained using the median, highest, and lowest weights. We describe a new methodology for the valuation of complications following VIH that allows a direct outcome comparison of procedures with different complication profiles. Further testing of the validity, reliability, and generalizability of this method is warranted.

Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.

PubMed

Figueroa, Rosa L; Flores, Christopher A

2016-08-01

Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naïve Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naïve Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation.

Inter and intra-observer reliability in assessment of the position of the lateral sesamoid in determining the severity of hallux valgus.

PubMed

Panchani, Sunil; Reading, Jonathan; Mehta, Jaysheel

2016-06-01

The position of the lateral sesamoid on standard dorso-plantar weight bearing radiographs, with respect to the lateral cortex of the first metatarsal, has been shown to correlate well with the degree of the hallux valgus angle. This study aimed to assess the inter- and intra-observer error of this new classification system. Five orthopaedic consultants and five trainee orthopaedic surgeons were recruited to assess and document the degree of displacement of the lateral sesamoid on 144 weight-bearing dorso-plantar radiographs on two separate occasions. The severity of hallux valgus was defined as normal (0%), mild (≤50%), moderate (51-≤99%) or severe (≥100%) depending on the percentage displacement of the lateral sesamoid body from the lateral cortical border of the first metatarsal. Consultant intra-observer variability showed good agreement between repeated assessment of the radiographs (mean Kappa=0.75). Intra-observer variability for trainee orthopaedic surgeons also showed good agreement with a mean Kappa=0.73. Intraclass correlations for consultants and trainee surgeons was also high. The new classification system of assessing the severity of hallux valgus shows high inter- and intra-observer variability with good agreement and reproducibility between surgeons of consultant and trainee grades. Copyright © 2015 Elsevier Ltd. All rights reserved.

Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification

DTIC Science & Technology

1999-05-17

Experimental Results In this section, we compare kNN -mut which uses the weight vector obtained using mutual information as the fi- nal weight vector and...WAKNN against kNN , C4.5 [Qui93], RIPPER [Coh95], PEBLS [CS93], Rainbow [McC96], VSM [Low95] on several synthetic and real data sets. VSM is another k...obtained without this option. 3 C4.5 RIPPER PEBLS Rainbow kNN WAKNN Syn-1 100.0 100.0 100.0 100.0 77.3 100.0 Syn-2 67.5 69.5 62.0 50.0 66.0 68.8 Syn

Weight loss in a patient with polycystic kidney disease: when liver cysts are no longer innocent bystanders.

PubMed

Cecere, N; Hakem, S; Demoulin, N; Hubert, C; Jabbour, N; Goffette, P; Pirson, Y; Morelle, J

2015-10-01

Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent inherited kidney disorder, and liver involvement represents one of its major extra-renal manifestations. Although asymptomatic in most patients, polycystic liver disease (PLD) can lead to organ compression, severe disability and even become life-threatening, thereby warranting early recognition and appropriate management. We report the case of a 56-year-old woman with ADPKD and severe weight loss secondary to a giant hepatic cyst compressing the pylorus. Partial hepatectomy was required after failure of cyst aspiration and sclerotherapy, and patient's condition improved rapidly. We discuss the presentation and classification of compressing liver cysts, and the available therapeutic alternatives for this potentially severe complication of ADPKD.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

Definition and classification of cancer cachexia: an international consensus.

PubMed

Fearon, Kenneth; Strasser, Florian; Anker, Stefan D; Bosaeus, Ingvar; Bruera, Eduardo; Fainsinger, Robin L; Jatoi, Aminah; Loprinzi, Charles; MacDonald, Neil; Mantovani, Giovanni; Davis, Mellar; Muscaritoli, Maurizio; Ottery, Faith; Radbruch, Lukas; Ravasco, Paula; Walsh, Declan; Wilcock, Andrew; Kaasa, Stein; Baracos, Vickie E

2011-05-01

To develop a framework for the definition and classification of cancer cachexia a panel of experts participated in a formal consensus process, including focus groups and two Delphi rounds. Cancer cachexia was defined as a multifactorial syndrome defined by an ongoing loss of skeletal muscle mass (with or without loss of fat mass) that cannot be fully reversed by conventional nutritional support and leads to progressive functional impairment. Its pathophysiology is characterised by a negative protein and energy balance driven by a variable combination of reduced food intake and abnormal metabolism. The agreed diagnostic criterion for cachexia was weight loss greater than 5%, or weight loss greater than 2% in individuals already showing depletion according to current bodyweight and height (body-mass index [BMI] <20 kg/m(2)) or skeletal muscle mass (sarcopenia). An agreement was made that the cachexia syndrome can develop progressively through various stages--precachexia to cachexia to refractory cachexia. Severity can be classified according to degree of depletion of energy stores and body protein (BMI) in combination with degree of ongoing weight loss. Assessment for classification and clinical management should include the following domains: anorexia or reduced food intake, catabolic drive, muscle mass and strength, functional and psychosocial impairment. Consensus exists on a framework for the definition and classification of cancer cachexia. After validation, this should aid clinical trial design, development of practice guidelines, and, eventually, routine clinical management. Copyright © 2011 Elsevier Ltd. All rights reserved.

Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.

PubMed

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

2017-02-15

Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. The proposed approach is a promising candidate for future BCI systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Literature-based concept profiles for gene annotation: the issue of weighting.

PubMed

Jelier, Rob; Schuemie, Martijn J; Roes, Peter-Jan; van Mulligen, Erik M; Kors, Jan A

2008-05-01

Text-mining has been used to link biomedical concepts, such as genes or biological processes, to each other for annotation purposes or the generation of new hypotheses. To relate two concepts to each other several authors have used the vector space model, as vectors can be compared efficiently and transparently. Using this model, a concept is characterized by a list of associated concepts, together with weights that indicate the strength of the association. The associated concepts in the vectors and their weights are derived from a set of documents linked to the concept of interest. An important issue with this approach is the determination of the weights of the associated concepts. Various schemes have been proposed to determine these weights, but no comparative studies of the different approaches are available. Here we compare several weighting approaches in a large scale classification experiment. Three different techniques were evaluated: (1) weighting based on averaging, an empirical approach; (2) the log likelihood ratio, a test-based measure; (3) the uncertainty coefficient, an information-theory based measure. The weighting schemes were applied in a system that annotates genes with Gene Ontology codes. As the gold standard for our study we used the annotations provided by the Gene Ontology Annotation project. Classification performance was evaluated by means of the receiver operating characteristics (ROC) curve using the area under the curve (AUC) as the measure of performance. All methods performed well with median AUC scores greater than 0.84, and scored considerably higher than a binary approach without any weighting. Especially for the more specific Gene Ontology codes excellent performance was observed. The differences between the methods were small when considering the whole experiment. However, the number of documents that were linked to a concept proved to be an important variable. When larger amounts of texts were available for the generation of the concepts' vectors, the performance of the methods diverged considerably, with the uncertainty coefficient then outperforming the two other methods.

[Classification of Colombian children with malnutrition according to NCHS reference or WHO standard].

PubMed

Velásquez, Claudia; Bermúdez, Juliana; Echeverri, Claudia; Estrada, Alejandro

2011-12-01

A descriptive study was conducted to evaluate the concordance of National Center for Health Statistics reference (NCHS) used to classify undernourished children from Colombia with the WHO Child Growth Standards. We used data from children aged 6 to 59 months with acute malnutrition (Z <-2) and severe (Z <-3) who were admitted to the "Unidad Vida Infantil" nutrition program in Colombia. Indicators height-for-age, weight for-height were analyzed when they were admitted to the hospital and weight for-height leaving the hospital. A statistical method used to compare means was T-student. Correlation coefficient intraclass (CCI) and Kappa index evaluated the concordance between NCHS and OMS; McNemar method evaluated the changes on the nutritional classification for children according to growth devices used. Of the total number of children classified as normal by NCHS, 10.4% were classified as stunted by WHO. 64% of the children admitted to the hospital presented acute malnutrition according to NCHS, of these 44,8% presented severe emaciation according to OMS, indeed severe emaciation increased of 36,0% to 63,3% using OMS. 5% of children leaving the hospital could need to stay more days if they had been evaluated with OMS. Growth devices shown high concordance in height-for-age (CCI = 0,988; k= 0,866) and weight for-height (CCI = 0,901; k = 0,578). Concluded that OMS growth standards classified more malnourished children and more severe states, in addition more malnourished children could be hospitalized and they could stay more days.

Vessel classification in overhead satellite imagery using weighted "bag of visual words"

NASA Astrophysics Data System (ADS)

Parameswaran, Shibin; Rainey, Katie

2015-05-01

Vessel type classification in maritime imagery is a challenging problem and has applications to many military and surveillance applications. The ability to classify a vessel correctly varies significantly depending on its appearance which in turn is affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The difficulty in classifying vessels also varies among different ship types as some types of vessels show more within-class variation than others. In our previous work, we showed that the bag of visual words" (V-BoW) was an effective feature representation for this classification task in the maritime domain. The V-BoW feature representation is analogous to the bag of words" (BoW) representation used in information retrieval (IR) application in text or natural language processing (NLP) domain. It has been shown in the textual IR applications that the performance of the BoW feature representation can be improved significantly by applying appropriate term-weighting such as log term frequency, inverse document frequency etc. Given the close correspondence between textual BoW (T-BoW) and V-BoW feature representations, we propose to apply several well-known term weighting schemes from the text IR domain on V-BoW feature representation to increase its ability to discriminate between ship types.

Classification of feeding and eating disorders: review of evidence and proposals for ICD-11

PubMed Central

UHER, RUDOLF; RUTTER, MICHAEL

2012-01-01

Current classification of eating disorders is failing to classify most clinical presentations; ignores continuities between child, adolescent and adult manifestations; and requires frequent changes of diagnosis to accommodate the natural course of these disorders. The classification is divorced from clinical practice, and investigators of clinical trials have felt compelled to introduce unsystematic modifications. Classification of feeding and eating disorders in ICD-11 requires substantial changes to remediate the shortcomings. We review evidence on the developmental and cross-cultural differences and continuities, course and distinctive features of feeding and eating disorders. We make the following recommendations: a) feeding and eating disorders should be merged into a single grouping with categories applicable across age groups; b) the category of anorexia nervosa should be broadened through dropping the requirement for amenorrhoea, extending the weight criterion to any significant underweight, and extending the cognitive criterion to include developmentally and culturally relevant presentations; c) a severity qualifier “with dangerously low body weight” should distinguish the severe cases of anorexia nervosa that carry the riskiest prognosis; d) bulimia nervosa should be extended to include subjective binge eating; e) binge eating disorder should be included as a specific category defined by subjective or objective binge eating in the absence of regular compensatory behaviour; f) combined eating disorder should classify subjects who sequentially or concurrently fulfil criteria for both anorexia and bulimia nervosa; g) avoidant/restrictive food intake disorder should classify restricted food intake in children or adults that is not accompanied by body weight and shape related psychopathology; h) a uniform minimum duration criterion of four weeks should apply. PMID:22654933

On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

NASA Technical Reports Server (NTRS)

Medina Revera, Edwin J.; Espinosa, Ramon Vasquez

1997-01-01

The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

Large margin nearest neighbor classifiers.

PubMed

Domeniconi, Carlotta; Gunopulos, Dimitrios; Peng, Jing

2005-07-01

The nearest neighbor technique is a simple and appealing approach to addressing classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. We propose a technique that computes a locally flexible metric by means of support vector machines (SVMs). The decision function constructed by SVMs is used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local feature weighting scheme. We formally show that our method increases the margin in the weighted space where classification takes place. Moreover, our method has the important advantage of online computational efficiency over competing locally adaptive techniques for nearest neighbor classification. We demonstrate the efficacy of our method using both real and simulated data.

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

NASA Astrophysics Data System (ADS)

Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Neurodevelopmental, health, and growth status at age 6 years of children with birth weights less than 1001 grams.

PubMed

Teplin, S W; Burchinal, M; Johnson-Martin, N; Humphry, R A; Kraybill, E N

1991-05-01

The neurodevelopmental, health, and growth outcomes for 28 six-year-old extremely low birth weight (ELBW) (birth weight less than 1001 gm) children were compared with those of 26 control children born at term. The two groups did not differ in mean weight or height, but the ELBW group had smaller head circumferences (p = 0.015). Kaufman mental processing scores correlated with head circumference (p = 0.0003). Significantly more of the ELBW children (61%) had mild or moderate to severe neurologic problems compared with control children (23%) (p = 0.003). Three ELBW children had mild spastic diplegia; one was blind. Eighteen (64%) of the ELBW children had required rehospitalization versus five (20%) of the comparison group. The mean Kaufman Mental Processing Composite was lower for the ELBW group, but when the data were analyzed by maternal education, only those children whose mothers had a twelfth-grade education had significantly lower scores (p = 0.0001). A similar pattern of group differences was seen for scores on visual-motor function (p = 0.0045), visual-perceptual abilities (p = 0.003), and attention span (p = 0.0001). No group differences were seen regarding hyperactivity or parental stress. Overall functional disability among the ELBW children was considered absent in 46%, mild in 36%, and moderate to severe in 18%. There was a significant association (p = 0.029) between classification of handicap at 12 to 34 months and classification at 6 years. No neonatal factors correlated with 6-year outcome. A significant proportion of ELBW children had no severe disabilities, but many had dysfunctions likely to affect learning and behavior in school.

Crop Identification Technology Assessment for Remote Sensing (CITARS)

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

Distinguishing between tertiary and secondary facilities: a case study of cardiac diagnostic-related groups (DRGs).

PubMed

Rouse, Paul; Arulambalam, Ajit; Correa, Ralph; Ullman, Cornelia

2010-05-14

To develop a classification of tertiary cardiac DRGs in order to investigate differences in tertiary/secondary product mix across New Zealand district health boards (DHBs). 67 DRGs from 85,442 cardiac cases were analysed using cost weights and patient comorbidity complexity levels, which were used as a proxy for complexity. The research found high variability of severity within some DRGs. 5 DHBs are the main providers of 27 DRGs which are high cost and identified as tertiary by several ADHB clinicians; the same 5 DHBs have on average higher severity by DRG than the other DHBs. NZ tertiary hospitals have a product mix of DRGs with higher complexity than secondary hospitals. Funding based on case weights needs to recognise the additional resource requirements for this higher complexity.

1984–2010 trends in fire burn severity and area for the conterminous US

USGS Publications Warehouse

Picotte, Joshua J.; Peterson, Birgit E.; Meier, Gretchen; Howard, Stephen M.

2016-01-01

Burn severity products created by the Monitoring Trends in Burn Severity (MTBS) project were used to analyse historical trends in burn severity. Using a severity metric calculated by modelling the cumulative distribution of differenced Normalized Burn Ratio (dNBR) and Relativized dNBR (RdNBR) data, we examined burn area and burn severity of 4893 historical fires (1984–2010) distributed across the conterminous US (CONUS) and mapped by MTBS. Yearly mean burn severity values (weighted by area), maximum burn severity metric values, mean area of burn, maximum burn area and total burn area were evaluated within 27 US National Vegetation Classification macrogroups. Time series assessments of burned area and severity were performed using Mann–Kendall tests. Burned area and severity varied by vegetation classification, but most vegetation groups showed no detectable change during the 1984–2010 period. Of the 27 analysed vegetation groups, trend analysis revealed burned area increased in eight, and burn severity has increased in seven. This study suggests that burned area and severity, as measured by the severity metric based on dNBR or RdNBR, have not changed substantially for most vegetation groups evaluated within CONUS.

Weighted statistical parameters for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rimoldini, Lorenzo

2014-01-01

Unevenly spaced time series are common in astronomy because of the day-night cycle, weather conditions, dependence on the source position in the sky, allocated telescope time and corrupt measurements, for example, or inherent to the scanning law of satellites like Hipparcos and the forthcoming Gaia. Irregular sampling often causes clumps of measurements and gaps with no data which can severely disrupt the values of estimators. This paper aims at improving the accuracy of common statistical parameters when linear interpolation (in time or phase) can be considered an acceptable approximation of a deterministic signal. A pragmatic solution is formulated in terms of a simple weighting scheme, adapting to the sampling density and noise level, applicable to large data volumes at minimal computational cost. Tests on time series from the Hipparcos periodic catalogue led to significant improvements in the overall accuracy and precision of the estimators with respect to the unweighted counterparts and those weighted by inverse-squared uncertainties. Automated classification procedures employing statistical parameters weighted by the suggested scheme confirmed the benefits of the improved input attributes. The classification of eclipsing binaries, Mira, RR Lyrae, Delta Cephei and Alpha2 Canum Venaticorum stars employing exclusively weighted descriptive statistics achieved an overall accuracy of 92 per cent, about 6 per cent higher than with unweighted estimators.

Classification of Unmanned Aircraft Systems. UAS Classification/Categorization for Certification

NASA Technical Reports Server (NTRS)

2004-01-01

Category, class, and type designations are primary means to identify appropriate aircraft certification basis, operating rules/limitations, and pilot qualifications to operate in the National Airspace System (NAS). The question is whether UAS fit into existing aircraft categories or classes, or are unique enough to justify the creation of a new category/class. In addition, the characteristics or capabilities, which define when an UAS becomes a regulated aircraft, must also be decided. This issue focuses on UAS classification for certification purposes. Several approaches have been considered for classifying UAS. They basically group into either using a weight/mass basis, or a safety risk basis, factoring in the performance of the UAS, including where the UAS would operate. Under existing standards, aircraft must have a Type Certificate and Certificate of Airworthiness, in order to be used for "compensation or hire", a major difference from model aircraft. Newer technologies may make it possible for very small UAS to conduct commercial services, but that is left for a future discussion to extend the regulated aircraft to a lower level. The Access 5 position is that UAS are aircraft and should be regulated above the weight threshold differentiating them from model airplanes. The recommended classification grouping is summarized in a chart.

Expert Exchange Workgroup on Children Aged 5 and Younger with Severe Obesity: A Narrative Review of Medical and Genetic Risk Factors.

PubMed

Mirza, Nazrat; Phan, Thao-Ly; Tester, June; Fals, Angela; Fernandez, Cristina; Datto, George; Estrada, Elizabeth; Eneli, Ihuoma

2018-05-23

Severe obesity defined as an age- and gender-specific body mass index ≥120% of the 95th percentile in children younger than 5 years is well recognized as a significant challenge for prevention and treatment. This article provides an overview of the prevalence, classification of obesity severity, patterns of weight gain trajectory, medical and genetic risk factors, and comorbid disorders among young children with an emphasis on severe obesity. Studies suggest rapid weight gain trajectory in infancy, maternal smoking, maternal gestational diabetes, and genetic conditions are associated with an increased risk for severe obesity in early childhood. Among populations of young children with severe obesity seeking care, co-morbid conditions such as dyslipidemia and fatty liver disease are present and families report behavioral concerns and developmental delays. Children with severe obesity by age 5 represent a vulnerable population of children at high medical risk and need to be identified early and appropriately managed.

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

PubMed

Campbell, J Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). We developed 2 datasets of clinical images as part of the Imaging and Informatics in ROP study and determined a consensus reference standard diagnosis (RSD) for each image based on 3 independent image graders and the clinical examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units. Expert classification of images of plus disease in ROP. Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement). There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R 2  = 0.82; and dataset B: P < 0.05 and adjusted R 2  = 0.6615). There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different cut points for the amounts of vascular abnormality required for presence of plus and preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Parental Perceptions of Their Adolescent's Weight Status: The ECHO Study

ERIC Educational Resources Information Center

Hearst, Mary O.; Sherwood, Nancy E.; Klein, Elizabeth G.; Pasch, Keryn E.; Lytle, Leslie A.

2011-01-01

Objectives: To assess the correlates of parental classification of adolescent weight status. Methods: Measured adolescent weight status was compared to parent self-report perception data (n 374 dyads) using multivariate analyses with interactions to identify characteristics associated with inaccurate parent classification of adolescent weight…

A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.

PubMed

Grassmann, Felix; Mengelkamp, Judith; Brandl, Caroline; Harsch, Sebastian; Zimmermann, Martina E; Linkohr, Birgit; Peters, Annette; Heid, Iris M; Palm, Christoph; Weber, Bernhard H F

2018-04-10

Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%-92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly. Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age. Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Investigating Perceived vs. Medical Weight Status Classification among College Students: Room for Improvement Exists among the Overweight and Obese

ERIC Educational Resources Information Center

Duffrin, Christopher; Eakin, Angela; Bertrand, Brenda; Barber-Heidel, Kimberly; Carraway-Stage, Virginia

2011-01-01

The American College Health Association estimated that 31% of college students are overweight or obese. It is important that students have a correct perception of body weight status as extra weight has potential adverse health effects. This study assessed accuracy of perceived weight status versus medical classification among 102 college students.…

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18 F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

WND-CHARM: Multi-purpose image classification using compound image transforms

PubMed Central

Orlov, Nikita; Shamir, Lior; Macura, Tomasz; Johnston, Josiah; Eckley, D. Mark; Goldberg, Ilya G.

2008-01-01

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier’s high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org. PMID:18958301

Classification of human coronary atherosclerotic plaques using ex vivo high-resolution multicontrast-weighted MRI compared with histopathology.

PubMed

Li, Tao; Li, Xin; Zhao, Xihai; Zhou, Weihua; Cai, Zulong; Yang, Li; Guo, Aitao; Zhao, Shaohong

2012-05-01

The objective of our study was to evaluate the feasibility of ex vivo high-resolution multicontrast-weighted MRI to accurately classify human coronary atherosclerotic plaques according to the American Heart Association classification. Thirteen human cadaver heart specimens were imaged using high-resolution multicontrast-weighted MR technique (T1-weighted, proton density-weighted, and T2-weighted). All MR images were matched with histopathologic sections according to the landmark of the bifurcation of the left main coronary artery. The sensitivity and specificity of MRI for the classification of plaques were determined, and Cohen's kappa analysis was applied to evaluate the agreement between MRI and histopathology in the classification of atherosclerotic plaques. One hundred eleven MR cross-sectional images obtained perpendicular to the long axis of the proximal left anterior descending artery were successfully matched with the histopathologic sections. For the classification of plaques, the sensitivity and specificity of MRI were as follows: type I-II (near normal), 60% and 100%; type III (focal lipid pool), 80% and 100%; type IV-V (lipid, necrosis, fibrosis), 96.2% and 88.2%; type VI (hemorrhage), 100% and 99.0%; type VII (calcification), 93% and 100%; and type VIII (fibrosis without lipid core), 100% and 99.1%, respectively. Isointensity, which indicates lipid composition on histopathology, was detected on MRI in 48.8% of calcified plaques. Agreement between MRI and histopathology for plaque classification was 0.86 (p < 0.001). Ex vivo high-resolution multicontrast-weighted MRI can accurately classify advanced atherosclerotic plaques in human coronary arteries.

Minimum Expected Risk Estimation for Near-neighbor Classification

DTIC Science & Technology

2006-04-01

We consider the problems of class probability estimation and classification when using near-neighbor classifiers, such as k-nearest neighbors ( kNN ...estimate for weighted kNN classifiers with different prior information, for a broad class of risk functions. Theory and simulations show how significant...the difference is compared to the standard maximum likelihood weighted kNN estimates. Comparisons are made with uniform weights, symmetric weights

Nutritional and immunisation status, weaning practices and socio-economic conditions of under five children in three villages of Bangladesh.

PubMed

Iqbal Hossain, M; Yasmin, R; Kabir, I

1999-01-01

A total of 479 children aged 6-60 months (male/female, 240/239) were studies during 1991 to 1992. Weight for age, height for age (mean +/- SD) were 72 +/- 11%, 90 +/- 7 and 87 +/- 10% of NCHS median respectively. According to Gomez classification, 96% of children had varying degrees of protein energy malnutrition (PEM) (28.4% mild, 58.2% moderate and 9.2% severe). According to Waterlow classification 84% were stunted(36% mild, 33% moderate and 15% severe) and 67% were wasted (47% mild, 18% moderate and 2% severe). Of all children 368 (77%) received BCG and 439 (82%) received partial or full dose of DPT and Polio vaccines. Among children aged 13-60 months 75% received Measles vaccine. Weaning food was started at (mean +/- SD) 8 +/- 4 months. Low household income, parental illiteracy, small family size (< or = 6), early or late weaning and absence of BCG vaccination were significantly associated with severe PEM. Timely weaning, education and promotion of essential vaccination may reduce childhood malnutrition especially severe PEM.

Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries.

PubMed

Wangensteen, Arnlaug; Tol, Johannes L; Roemer, Frank W; Bahr, Roald; Dijkstra, H Paul; Crema, Michel D; Farooq, Abdulaziz; Guermazi, Ali

2017-04-01

To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Male athletes (n=40) with clinical diagnosis of acute hamstring injury and MRI ≤5days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. We observed 'substantial' to 'almost perfect' intra- (κ range 0.65-1.00) and interrater reliability (κ range 0.77-1.00) with percentage agreement 83-100% and 88-100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range -0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated 'substantial' to 'almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of Race and Ethnicity Classification on Survey Estimates: Anomaly of the Weighted Totals of American Indians and Alaska Natives

ERIC Educational Resources Information Center

Lee, Sunghee; Satter, Delight E.; Ponce, Ninez A.

2009-01-01

Racial classification is a paramount concern in data collection and analysis for American Indians and Alaska Natives (AI/ANs) and has far-reaching implications in health research. We examine how different racial classifications affect survey weights and consequently change health-related indicators for the AI/AN population in California. Using a…

Maternal weight change between 1 and 2 years postpartum: the importance of 1 year weight retention.

PubMed

Lipsky, Leah M; Strawderman, Myla S; Olson, Christine M

2012-07-01

Pregnancy weight gain may lead to long-term increases in maternal BMI for some women. The objective of this study was to examine maternal body weight change 1y-2y postpartum, and to compare classifications of 2y weight retention with and without accounting for 1y-2y weight gain. Early pregnancy body weight (EPW, first trimester) was measured or imputed, and follow-up measures obtained before delivery, 1 year postpartum (1y) and 2 years postpartum (2y) in an observational cohort study of women seeking prenatal care in several counties in upstate New York (n = 413). Baseline height was measured; demographic and behavioral data were obtained from questionnaires and medical records. Associations of 1y-2y weight change (kg) and 1y-2y weight gain (≥2.25 kg) with anthropometric, socioeconomic, and behavioral variables were evaluated using linear and logistic regressions. While mean ± SE 1y-2y weight change was 0.009 ± 4.6 kg, 1y-2y weight gain (≥2.25 kg) was common (n = 108, 26%). Odds of weight gain 1y-2y were higher for overweight (OR(adj) = 2.63, CI(95%) = 1.43-4.82) and obese (OR(adj) = 2.93, CI(95%) = 1.62-5.27) women than for women with BMI <25. Two year weight retention (2y-EPW ≥2.25 kg) was misclassified in 38% (n = 37) of women when 1y-2y weight gain was ignored. One year weight retention (1YWR) (1y-EPW) was negatively related to 1y-2y weight change (β(adj) ± SE = -0.28 ± 0.04, P < 0.001) and weight gain (≥2.25 kg) (OR(adj) = 0.91, CI(95%) = 0.87-0.95). Relations between 1y weight retention and 1y-2y weight change were attenuated for women with higher early pregnancy BMI. Weight change 1y-2y was predicted primarily by an inverse relation with 1y weight retention. The high frequency of weight gain has important implications for classification of postpartum weight retention.

Maternal Weight Change Between 1 and 2 Years Postpartum: The Importance of 1 Year Weight Retention

PubMed Central

Lipsky, Leah M.; Strawderman, Myla S.; Olson, Christine M.

2016-01-01

Pregnancy weight gain may lead to long-term increases in maternal BMI for some women. The objective of this study was to examine maternal body weight change 1y–2y postpartum, and to compare classifications of 2y weight retention with and without accounting for 1y–2y weight gain. Early pregnancy body weight (EPW, first trimester) was measured or imputed, and follow-up measures obtained before delivery, 1 year postpartum (1y) and 2 years postpartum (2y) in an observational cohort study of women seeking prenatal care in several counties in upstate New York (n = 413). Baseline height was measured; demographic and behavioral data were obtained from questionnaires and medical records. Associations of 1y–2y weight change (kg) and 1y–2y weight gain (≥2.25 kg) with anthropometric, socioeconomic, and behavioral variables were evaluated using linear and logistic regressions. While mean ± SE 1y–2y weight change was 0.009 ± 4.6 kg, 1y–2y weight gain (≥2.25 kg) was common (n = 108, 26%). Odds of weight gain 1y–2y were higher for overweight (ORadj = 2.63, CI95% = 1.43–4.82) and obese (ORadj = 2.93, CI95% = 1.62–5.27) women than for women with BMI <25. Two year weight retention (2y–EPW ≥2.25 kg) was misclassified in 38% (n = 37) of women when 1y–2y weight gain was ignored. One year weight retention (1YWR) (1y–EPW) was negatively related to 1y–2y weight change (βadj ± SE = −0.28 ± 0.04, P < 0.001) and weight gain (≥2.25 kg) (ORadj = 0.91, CI95% = 0.87–0.95). Relations between 1y weight retention and 1y–2y weight change were attenuated for women with higher early pregnancy BMI. Weight change 1y–2y was predicted primarily by an inverse relation with 1y weight retention. The high frequency of weight gain has important implications for classification of postpartum weight retention. PMID:22334257

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.

Automatic classification of killer whale vocalizations using dynamic time warping.

PubMed

Brown, Judith C; Miller, Patrick J O

2007-08-01

A set of killer whale sounds from Marineland were recently classified automatically [Brown et al., J. Acoust. Soc. Am. 119, EL34-EL40 (2006)] into call types using dynamic time warping (DTW), multidimensional scaling, and kmeans clustering to give near-perfect agreement with a perceptual classification. Here the effectiveness of four DTW algorithms on a larger and much more challenging set of calls by Northern Resident whales will be examined, with each call consisting of two independently modulated pitch contours and having considerable overlap in contours for several of the perceptual call types. Classification results are given for each of the four algorithms for the low frequency contour (LFC), the high frequency contour (HFC), their derivatives, and weighted sums of the distances corresponding to LFC with HFC, LFC with its derivative, and HFC with its derivative. The best agreement with the perceptual classification was 90% attained by the Sakoe-Chiba algorithm for the low frequency contours alone.

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

PubMed

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

Parallel consensual neural networks.

PubMed

Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

1997-01-01

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

Support for linguistic macrofamilies from weighted sequence alignment

PubMed Central

Jäger, Gerhard

2015-01-01

Computational phylogenetics is in the process of revolutionizing historical linguistics. Recent applications have shed new light on controversial issues, such as the location and time depth of language families and the dynamics of their spread. So far, these approaches have been limited to single-language families because they rely on a large body of expert cognacy judgments or grammatical classifications, which is currently unavailable for most language families. The present study pursues a different approach. Starting from raw phonetic transcription of core vocabulary items from very diverse languages, it applies weighted string alignment to track both phonetic and lexical change. Applied to a collection of ∼1,000 Eurasian languages and dialects, this method, combined with phylogenetic inference, leads to a classification in excellent agreement with established findings of historical linguistics. Furthermore, it provides strong statistical support for several putative macrofamilies contested in current historical linguistics. In particular, there is a solid signal for the Nostratic/Eurasiatic macrofamily. PMID:26403857

Algorithm for optimizing bipolar interconnection weights with applications in associative memories and multitarget classification.

PubMed

Chang, S; Wong, K W; Zhang, W; Zhang, Y

1999-08-10

An algorithm for optimizing a bipolar interconnection weight matrix with the Hopfield network is proposed. The effectiveness of this algorithm is demonstrated by computer simulation and optical implementation. In the optical implementation of the neural network the interconnection weights are biased to yield a nonnegative weight matrix. Moreover, a threshold subchannel is added so that the system can realize, in real time, the bipolar weighted summation in a single channel. Preliminary experimental results obtained from the applications in associative memories and multitarget classification with rotation invariance are shown.

Algorithm for Optimizing Bipolar Interconnection Weights with Applications in Associative Memories and Multitarget Classification

NASA Astrophysics Data System (ADS)

Chang, Shengjiang; Wong, Kwok-Wo; Zhang, Wenwei; Zhang, Yanxin

1999-08-01

An algorithm for optimizing a bipolar interconnection weight matrix with the Hopfield network is proposed. The effectiveness of this algorithm is demonstrated by computer simulation and optical implementation. In the optical implementation of the neural network the interconnection weights are biased to yield a nonnegative weight matrix. Moreover, a threshold subchannel is added so that the system can realize, in real time, the bipolar weighted summation in a single channel. Preliminary experimental results obtained from the applications in associative memories and multitarget classification with rotation invariance are shown.

Optimal weighted averaging of event related activity from acquisitions with artifacts.

PubMed

Vollero, Luca; Petrichella, Sara; Innello, Giulio

2016-08-01

In several biomedical applications that require the signal processing of biological data, the starting procedure for noise reduction is the ensemble averaging of multiple repeated acquisitions (trials). This method is based on the assumption that each trial is composed of two additive components: (i) a time-locked activity related to some sensitive/stimulation phenomenon (ERA, Event Related Activity in the following) and (ii) a sum of several other non time-locked background activities. The averaging aims at estimating the ERA activity under very low Signal to Noise and Interference Ratio (SNIR). Although averaging is a well established tool, its performance can be improved in the presence of high-power disturbances (artifacts) by a trials classification and removal stage. In this paper we propose, model and evaluate a new approach that avoids trials removal, managing trials classified as artifact-free and artifact-prone with two different weights. Based on the model, a weights tuning is possible and through modeling and simulations we show that, when optimally configured, the proposed solution outperforms classical approaches.

Overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three body mass index classification systems.

PubMed

St-Jean, Audray; Meziou, Salma; Ayotte, Pierre; Lucas, Michel

2017-11-22

Little is known about the suitability of three commonly used body mass index (BMI) classification systems for Indigenous youth. We estimated overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three BMI classification systems, assessed the level of agreement between them, and evaluated their accuracy through body fat and cardiometabolic risk factors. Data on 288 youth (aged 8-17 years) were collected. Overweight and obesity prevalence were estimated with Centers for Disease Control and Prevention (CDC), International Obesity Task Force (IOTF) and World Health Organization (WHO) criteria. Agreement was measured with weighted kappa (κw). Associations with body fat and cardiometabolic risk factors were evaluated by analysis of variance. Obesity prevalence was 42.7% with IOTF, 47.2% with CDC, and 49.3% with WHO criteria. Agreement was almost perfect between IOTF and CDC (κw = 0.93), IOTF and WHO (κw = 0.91), and WHO and CDC (κw = 0.94). Means of body fat and cardiometabolic risk factors were significantly higher (P trend  < 0.001) from normal weight to obesity, regardless of the system used. Youth considered overweight by IOTF but obese by CDC or WHO exhibited less severe clinical obesity. IOTF seems to be more accurate in identifying obesity in Cree youth.

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

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.

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

Influence of Weight Classification on Walking and Jogging Energy Expenditure Prediction in Women

ERIC Educational Resources Information Center

Heden, Timothy D.; LeCheminant, James D.; Smith, John D.

2012-01-01

The purpose of this study was to determine the influence of weight classification on predicting energy expenditure (EE) in women. Twelve overweight (body mass index [BMI] = 25-29.99 kg/m[superscript 2]) and 12 normal-weight (BMI = 18.5-24.99 kg/m[superscript 2]) women walked and jogged 1,609 m at 1.34 m.s[superscript -1] and 2.23 m.s[superscript…

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 42 Public Health 2 2013-10-01 2013-10-01 false DRG classification and weighting factors. 412.60... discharge is based, as appropriate, on the patient's age, sex, principal diagnosis (that is, the diagnosis...), secondary diagnoses, procedures performed, and discharge status. (2) Each discharge is assigned to only one...

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 42 Public Health 2 2012-10-01 2012-10-01 false DRG classification and weighting factors. 412.60... discharge is based, as appropriate, on the patient's age, sex, principal diagnosis (that is, the diagnosis...), secondary diagnoses, procedures performed, and discharge status. (2) Each discharge is assigned to only one...

A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease.

PubMed

Nancy Jane, Y; Khanna Nehemiah, H; Arputharaj, Kannan

2016-04-01

Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms. Copyright © 2016 Elsevier Inc. All rights reserved.

Weight status of indigenous youth in Oaxaca, southern Mexico: concordance of IOTF and WHO criteria.

PubMed

Malina, Robert M; Peйa Reyes, Maria Eugenia; Chávez, Guillermo Bali; Little, Bertis B

2013-01-01

To compare the prevalence of thinness, overweight and obesity with IOTF and WHO criteria among indigenous school youth from the state of Oaxaca, southern Mexico. The sample included 11 454 indigenous youth (6216 boys, 5238 girls) 6-14 years of age. Heights and weights were measured in 2007 by trained staff. BMIs were calculated and classified as severely thin, moderately thin, normal, overweight or obese using age- and sex-specific IOTF and WHO cut-offs. Prevalence, percentage agreement between classifications, Spearman rank order correlations and Kappa coefficients were calculated. Prevalence of overweight and obesity was higher with WHO than IOTF criteria, while prevalence of severe and moderate thinness did not appreciably differ between criteria. Weight status with the two criteria was discordant in 839 boys (13.5%) and 383 girls (7.3%) and more often for overweight and obesity than thinness. Percentage agreement, correlations and Kappa coefficients were moderate-to-high and were higher in girls than boys. Prevalence of overweight and obesity among indigenous youth in Oaxaca was higher with WHO than IOTF criteria, whereas prevalence of severe and moderate thinness was similar. Differences in estimates for overweight and obesity have implications for surveillance.

The nutritional status of hospitalized children: Has this subject been overlooked?

PubMed

Kapçı, Nermin; Akçam, Mustafa; Koca, Tuğba; Dereci, Selim; Kapcı, Mücahit

2015-07-01

To determine the nutritional status of hospitalized children at the time of admission and to investigate the relationship between diagnosis and nutritional status. Body weight, height, triceps skinfold thickness, and mid-arm circumference were measured on admission and percentages of weight-for-age, weight-for-height, body mass index, mid-arm circumference, and triceps skinfold thickness were calculated. The nutritional status was evaluated using the Waterlow, Gomez, and other anthropometric assessments. A total of 511 patients were included in the study with a mean age of 5.8±4.9 years. Malnutrition was determined in 52.7% of patients according to the Waterlow classification. Mild malnutrition was determined in 39%, moderate in 12%, and severe in 1.7%, with the characteristics of acute malnutrition in 23.9%, acute-chronic in 7.3%, and chronic in 21.5%. The highest rate of malnutrition was in the 0-2 years age group (62.3%). According to the Gomez classification, malnutrition rate was determined as 46.8%. The rates of malnutrition in malignant, gastrointestinal, and infectious diseases were 60%, 59.8%, and 54.5%, respectively. The prevalence of malnutrition in hospitalized children was noticeably high. The nutritional evaluation of all patients and an early start to nutritional support could provide a significant positive contribution.

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Size classifications. 51.2559 Section 51.2559... STANDARDS) United States Standards for Grades of Shelled Pistachio Nuts § 51.2559 Size classifications. (a... the following size classifications. (1) Jumbo Whole Kernels: 80 percent or more by weight shall be...

7 CFR 51.2559 - Size classifications.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Size classifications. 51.2559 Section 51.2559... STANDARDS) United States Standards for Grades of Shelled Pistachio Nuts § 51.2559 Size classifications. (a... the following size classifications. (1) Jumbo Whole Kernels: 80 percent or more by weight shall be...

Treatment Options for Severe Obesity in the Pediatric Population: Current Limitations and Future Opportunities.

PubMed

Ryder, Justin R; Fox, Claudia K; Kelly, Aaron S

2018-06-01

Severe obesity is the only obesity classification increasing in prevalence among children and adolescents. Treatment options that produce meaningful and sustained weight loss and comorbidity resolution are urgently needed. The purpose of this review is to provide a brief overview of the current treatment options for pediatric severe obesity and offer suggestions regarding future opportunities for accelerating the development and evaluation of innovative treatment strategies. At present, there are three treatment options for youth with severe obesity: lifestyle modification therapy, pharmacotherapy, and bariatric surgery. Lifestyle modification therapy can be useful for improving many chronic disease risk factors and comorbid conditions but often fails to achieve clinically meaningful and sustainable weight loss. Pharmacotherapy holds promise as an effective adjunctive treatment but remains in the primordial stages of development in the pediatric population. Bariatric surgery provides robust weight loss and risk factor/comorbidity improvements but is accompanied by higher risks and lower uptake compared to lifestyle modification therapy and pharmacotherapy. New areas worth pursuing include combination pharmacotherapy, device therapy, identification of predictors of response aimed at precision treatment, and interventions in the postbariatric surgical setting to improve long-term outcomes. Treating pediatric severe obesity effectively and safely is extremely challenging. Some progress has been made, but substantially more effort and innovation are needed in the future to combat this serious and ongoing medical and public health issue. © 2018 The Obesity Society.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

PubMed

Schouten, Tijn M; Koini, Marisa; de Vos, Frank; Seiler, Stephan; van der Grond, Jeroen; Lechner, Anita; Hafkemeijer, Anne; Möller, Christiane; Schmidt, Reinhold; de Rooij, Mark; Rombouts, Serge A R B

2016-01-01

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

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.

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

Five-way smoking status classification using text hot-spot identification and error-correcting output codes.

PubMed

Cohen, Aaron M

2008-01-01

We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using micro- and macro-averaged F1 as the primary performance metric. Our best performing system achieved a micro-F1 of 0.9000 on the test collection, equivalent to the best performing system submitted to the i2b2 challenge. Hot-spot identification, zero-vector filtering, classifier weighting, and error correcting output coding contributed additively to increased performance, with hot-spot identification having by far the largest positive effect. High performance on automatic identification of patient smoking status from discharge summaries is achievable with the efficient and straightforward machine learning techniques studied here.

Fitness Tracker for Weight Lifting Style Workouts

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

Wihl, B. M.

This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to thosemore » of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.« less

Variance approximations for assessments of classification accuracy

Treesearch

R. L. Czaplewski

1994-01-01

Variance approximations are derived for the weighted and unweighted kappa statistics, the conditional kappa statistic, and conditional probabilities. These statistics are useful to assess classification accuracy, such as accuracy of remotely sensed classifications in thematic maps when compared to a sample of reference classifications made in the field. Published...

Boosted classification trees result in minor to modest improvement in the accuracy in classifying cardiovascular outcomes compared to conventional classification trees

PubMed Central

Austin, Peter C; Lee, Douglas S

2011-01-01

Purpose: Classification trees are increasingly being used to classifying patients according to the presence or absence of a disease or health outcome. A limitation of classification trees is their limited predictive accuracy. In the data-mining and machine learning literature, boosting has been developed to improve classification. Boosting with classification trees iteratively grows classification trees in a sequence of reweighted datasets. In a given iteration, subjects that were misclassified in the previous iteration are weighted more highly than subjects that were correctly classified. Classifications from each of the classification trees in the sequence are combined through a weighted majority vote to produce a final classification. The authors' objective was to examine whether boosting improved the accuracy of classification trees for predicting outcomes in cardiovascular patients. Methods: We examined the utility of boosting classification trees for classifying 30-day mortality outcomes in patients hospitalized with either acute myocardial infarction or congestive heart failure. Results: Improvements in the misclassification rate using boosted classification trees were at best minor compared to when conventional classification trees were used. Minor to modest improvements to sensitivity were observed, with only a negligible reduction in specificity. For predicting cardiovascular mortality, boosted classification trees had high specificity, but low sensitivity. Conclusions: Gains in predictive accuracy for predicting cardiovascular outcomes were less impressive than gains in performance observed in the data mining literature. PMID:22254181

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

TFM classification and staging of oral submucous fibrosis: A new proposal.

PubMed

Arakeri, Gururaj; Thomas, Deepak; Aljabab, Abdulsalam S; Hunasgi, Santosh; Rai, Kirthi Kumar; Hale, Beverley; Fonseca, Felipe Paiva; Gomez, Ricardo Santiago; Rahimi, Siavash; Merkx, Matthias A W; Brennan, Peter A

2018-04-01

We have evaluated the rationale of existing grading and staging schemes of oral submucous fibrosis (OSMF) based on how they are categorized. A novel classification and staging scheme is proposed. A total of 300 OSMF patients were evaluated for agreement between functional, clinical, and histopathological staging. Bilateral biopsies were assessed in 25 patients to evaluate for any differences in histopathological staging of OSMF in the same mouth. Extent of clinician agreement for categorized staging data was evaluated using Cohen's weighted kappa analysis. Cross-tabulation was performed on categorical grading data to understand the intercorrelation, and the unweighted kappa analysis was used to assess the bilateral grade agreement. Probabilities of less than 0.05 were considered significant. Data were analyzed using SPSS Statistics (version 25.0, IBM, USA). A low agreement was found between all the stages depicting the independent nature of trismus, clinical features, and histopathological components (K = 0.312, 0.167, 0.152) in OSMF. Following analysis, a three-component classification scheme (TFM classification) was developed that describes the severity of each independently, grouping them using a novel three-tier staging scheme as a guide to the treatment plan. The proposed classification and staging could be useful for effective communication, categorization, and for recording data and prognosis, and for guiding treatment plans. Furthermore, the classification considers OSMF malignant transformation in detail. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Explaining resource consumption among non-normal neonates

PubMed Central

Schwartz, Rachel M.; Michelman, Thomas; Pezzullo, John; Phibbs, Ciaran S.

1991-01-01

The adoption by Medicare in 1983 of prospective payment using diagnosis-related groups (DRGs) has stimulated research to develop case-mix grouping schemes that more accurately predict resource consumption by patients. In this article, the authors explore a new method designed to improve case-mix classification for newborns through the use of birth weight in combination with DRGs to adjust the unexplained case-mix severity. Although the findings are developmental in nature, they reveal that the model significantly improves our ability to explain resource use. PMID:10122360

SNR-adaptive stream weighting for audio-MES ASR.

PubMed

Lee, Ki-Seung

2008-08-01

Myoelectric signals (MESs) from the speaker's mouth region have been successfully shown to improve the noise robustness of automatic speech recognizers (ASRs), thus promising to extend their usability in implementing noise-robust ASR. In the recognition system presented herein, extracted audio and facial MES features were integrated by a decision fusion method, where the likelihood score of the audio-MES observation vector was given by a linear combination of class-conditional observation log-likelihoods of two classifiers, using appropriate weights. We developed a weighting process adaptive to SNRs. The main objective of the paper involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. These two parameters were determined by a method based on a maximum mutual information criterion. Acoustic and facial MES data were collected from five subjects, using a 60-word vocabulary. Four types of acoustic noise including babble, car, aircraft, and white noise were acoustically added to clean speech signals with SNR ranging from -14 to 31 dB. The classification accuracy of the audio ASR was as low as 25.5%. Whereas, the classification accuracy of the MES ASR was 85.2%. The classification accuracy could be further improved by employing the proposed audio-MES weighting method, which was as high as 89.4% in the case of babble noise. A similar result was also found for the other types of noise.

Children with motor impairment related to cerebral palsy: Prevalence, severity and concurrent impairments in China.

PubMed

He, Ping; Chen, Gong; Wang, Zhenjie; Guo, Chao; Zheng, Xiaoying

2017-05-01

Cerebral palsy (CP) is the most common cause of motor impairment in childhood. This study aimed to examine the prevalence, severity and concurrent impairments of CP-related motor impairment among Chinese children. Children with CP-related motor impairment aged 0-17 years were identified through a national population-based survey based on World Health Organization International Classification of Functioning, Disability and Health. Logistic regression models allowing for weights were used to examine individual and family factors in relation to CP-related motor impairment. The weighted prevalence of CP-related motor impairment was 1.25 per 1000 children (95% confidence interval (CI): 1.16, 1.35) in China. Male children, children in multiples and in families where adults suffered from CP, were more likely to be affected by CP-related motor impairment. For mild, moderate, severe and extremely severe groups of motor impairment, weighted proportions of CP were 14.12% (95%CI: 11.70, 16.95), 20.35% (95%CI: 17.48, 23.56), 27.44% (95%CI: 24.25, 30.87) and 38.09% (95%CI: 34.55, 41.76), respectively; and weighted proportions of concurrent visual, hearing and cognitive impairment were 5.00% (95%CI: 3.59, 6.91), 6.98% (95%CI: 5.34, 9.08) and 71.06% (95%CI: 67.57, 74.31), respectively. Gender, multiple births and family adults with CP were significantly associated with CP-related motor impairment in Chinese children. Proportions of CP and concurrent impairments that increased with severity of motor impairment were observed. © 2017 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

[Classification of Priority Area for Soil Environmental Protection Around Water Sources: Method Proposed and Case Demonstration].

PubMed

Li, Lei; Wang, Tie-yu; Wang, Xiaojun; Xiao, Rong-bo; Li, Qi-feng; Peng, Chi; Han, Cun-liang

2016-04-15

Based on comprehensive consideration of soil environmental quality, pollution status of river, environmental vulnerability and the stress of pollution sources, a technical method was established for classification of priority area of soil environmental protection around the river-style water sources. Shunde channel as an important drinking water sources of Foshan City, Guangdong province, was studied as a case, of which the classification evaluation system was set up. In detail, several evaluation factors were selected according to the local conditions of nature, society and economy, including the pollution degree of heavy metals in soil and sediment, soil characteristics, groundwater sensitivity, vegetation coverage, the type and location of pollution sources. Data information was mainly obtained by means of field survey, sampling analysis, and remote sensing interpretation. Afterwards, Analytical Hierarchy Process (AHP) was adopted to decide the weight of each factor. The basic spatial data layers were set up respectively and overlaid based on the weighted summation assessment model in Geographical Information System (GIS), resulting in a classification map of soil environmental protection level in priority area of Shunde channel. Accordingly, the area was classified to three levels named as polluted zone, risky zone and safe zone, which respectively accounted for 6.37%, 60.90% and 32.73% of the whole study area. Polluted zone and risky zone were mainly distributed in Lecong, Longjiang and Leliu towns, with pollutants mainly resulted from the long-term development of aquaculture and the industries containing furniture, plastic constructional materials and textile and clothing. In accordance with the main pollution sources of soil, targeted and differentiated strategies were put forward. The newly established evaluation method could be referenced for the protection and sustainable utilization of soil environment around the water sources.

A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis.

PubMed

Gu, Dongxiao; Liang, Changyong; Zhao, Huimin

2017-03-01

We present the implementation and application of a case-based reasoning (CBR) system for breast cancer related diagnoses. By retrieving similar cases in a breast cancer decision support system, oncologists can obtain powerful information or knowledge, complementing their own experiential knowledge, in their medical decision making. We observed two problems in applying standard CBR to this context: the abundance of different types of attributes and the difficulty in eliciting appropriate attribute weights from human experts. We therefore used a distance measure named weighted heterogeneous value distance metric, which can better deal with both continuous and discrete attributes simultaneously than the standard Euclidean distance, and a genetic algorithm for learning the attribute weights involved in this distance measure automatically. We evaluated our CBR system in two case studies, related to benign/malignant tumor prediction and secondary cancer prediction, respectively. Weighted heterogeneous value distance metric with genetic algorithm for weight learning outperformed several alternative attribute matching methods and several classification methods by at least 3.4%, reaching 0.938, 0.883, 0.933, and 0.984 in the first case study, and 0.927, 0.842, 0.939, and 0.989 in the second case study, in terms of accuracy, sensitivity×specificity, F measure, and area under the receiver operating characteristic curve, respectively. The evaluation result indicates the potential of CBR in the breast cancer diagnosis domain. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing the impact of adjusting for maturity in weight status classification in a cross-sectional sample of UK children.

PubMed

Gillison, Fiona; Cumming, Sean; Standage, Martyn; Barnaby, Catherine; Katzmarzyk, Peter

2017-06-26

To compare the weight categorisation of a cohort of UK children using standard procedures (ie, comparing body mass index (BMI) centiles to age-matched UK reference data) versus an approach adjusted for maturation status (ie, matching relative to biological age). Analysis of data collected from an observational study of UK primary school children. Schools in South West England. Four hundred and seven 9-11 year-old children (98% white British). Weight status was classified using BMI centiles using (1) sex and chronological age-matched referents and (2) sex and biological age-matched referents (based on % of predicted adult stature) relative to UK 1990 reference growth charts. For both approaches, children were classified as a normal weight if >2nd centile and <85thcentile, overweight if 85th and <95thcentiles, and obese if ≥95thcentile. Fifty-one children (12.5%) were overweight, and a further 51 obese (12.5%) according to standard chronological age-matched classifications. Adjustment for maturity resulted in 32% of overweight girls, and 15% of overweight boys being reclassified as a normal weight, and 11% and 8% of obese girls and boys, respectively, being reclassified as overweight. Early maturing children were 4.9 times more likely to be reclassified from overweight to normal weight than 'on-time' maturers (OR 95% CI 1.3 to 19). Incorporating assessments of maturational status into weight classification resulted in significant changes to the classification of early-maturing adolescents. Further research exploring the implications for objective health risk and well-being is needed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Nutritional status of children and adolescents based on body mass index: agreement between World Health Organization and International Obesity Task Force

PubMed Central

Cavazzotto, Timothy Gustavo; Brasil, Marcos Roberto; Oliveira, Vinicius Machado; da Silva, Schelyne Ribas; Ronque, Enio Ricardo V.; Queiroga, Marcos Roberto; Serassuelo, Helio

2014-01-01

Objective: To investigate the agreement between two international criteria for classification of children and adolescents nutritional status. Methods: The study included 778 girls and 863 boys aged from six to 13 years old. Body mass and height were measured and used to calculate the body mass index. Nutritional status was classified according to the cut-off points defined by the World Health Organization and the International Obesity Task Force. The agreement was evaluated using Kappa statistic and weighted Kappa. Results: In order to classify the nutritional status, the agreement between the criteria was higher for the boys (Kappa 0.77) compared to girls (Kappa 0.61). The weighted Kappa was also higher for boys (0.85) in comparison to girls (0.77). Kappa index varied according to age. When the nutritional status was classified in only two categories - appropriate (thinness + accentuated thinness + eutrophy) and overweight (overweight + obesity + severe obesity) -, the Kappa index presented higher values than those related to the classification in six categories. Conclusions: A substantial agreement was observed between the criteria, being higher in males and varying according to the age. PMID:24676189

Optimal frame-by-frame result combination strategy for OCR in video stream

NASA Astrophysics Data System (ADS)

Bulatov, Konstantin; Lynchenko, Aleksander; Krivtsov, Valeriy

2018-04-01

This paper describes the problem of combining classification results of multiple observations of one object. This task can be regarded as a particular case of a decision-making using a combination of experts votes with calculated weights. The accuracy of various methods of combining the classification results depending on different models of input data is investigated on the example of frame-by-frame character recognition in a video stream. Experimentally it is shown that the strategy of choosing a single most competent expert in case of input data without irrelevant observations has an advantage (in this case irrelevant means with character localization and segmentation errors). At the same time this work demonstrates the advantage of combining several most competent experts according to multiplication rule or voting if irrelevant samples are present in the input data.

Item Selection Criteria with Practical Constraints for Computerized Classification Testing

ERIC Educational Resources Information Center

Lin, Chuan-Ju

2011-01-01

This study compares four item selection criteria for a two-category computerized classification testing: (1) Fisher information (FI), (2) Kullback-Leibler information (KLI), (3) weighted log-odds ratio (WLOR), and (4) mutual information (MI), with respect to the efficiency and accuracy of classification decision using the sequential probability…

40 CFR 86.085-20 - Incomplete vehicles, classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., classification. (a) An incomplete truck less than 8,500 pounds gross vehicle weight rating shall be classified by... 40 Protection of Environment 18 2010-07-01 2010-07-01 false Incomplete vehicles, classification... PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES General...

Evidence-based severity assessment: Impact of repeated versus single open-field testing on welfare in C57BL/6J mice.

PubMed

Bodden, Carina; Siestrup, Sophie; Palme, Rupert; Kaiser, Sylvia; Sachser, Norbert; Richter, S Helene

2018-01-15

According to current guidelines on animal experiments, a prospective assessment of the severity of each procedure is mandatory. However, so far, the classification of procedures into different severity categories mainly relies on theoretic considerations, since it is not entirely clear which of the various procedures compromise the welfare of animals, or, to what extent. Against this background, a systematic empirical investigation of the impact of each procedure, including behavioral testing, seems essential. Therefore, the present study was designed to elucidate the effects of repeated versus single testing on mouse welfare, using one of the most commonly used paradigms for behavioral phenotyping in behavioral neuroscience, the open-field test. In an independent groups design, laboratory mice (Mus musculus f. domestica) experienced either repeated, single, or no open-field testing - procedures that are assigned to different severity categories. Interestingly, testing experiences did not affect fecal corticosterone metabolites, body weights, elevated plus-maze or home cage behavior differentially. Thus, with respect to the assessed endocrinological, physical, and behavioral outcome measures, no signs of compromised welfare could be detected in mice that were tested in the open-field repeatedly, once, or, not at all. These findings challenge current classification guidelines and may, furthermore, stimulate systematic research on the severity of single procedures involving living animals. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density.

PubMed

Hodge, Rebecca; Hellmann, Sophie Sell; von Euler-Chelpin, My; Vejborg, Ilse; Andersen, Zorana Jovanovic

2014-06-01

In the Copenhagen mammography screening program from 1991 to 2001, mammographic density was classified either as fatty or mixed/dense. This dichotomous mammographic density classification system is unique internationally, and has not been validated before. To compare the Danish dichotomous mammographic density classification system from 1991 to 2001 with the density BI-RADS classifications, in an attempt to validate the Danish classification system. The study sample consisted of 120 mammograms taken in Copenhagen in 1991-2001, which tested false positive, and which were in 2012 re-assessed and classified according to the BI-RADS classification system. We calculated inter-rater agreement between the Danish dichotomous mammographic classification as fatty or mixed/dense and the four-level BI-RADS classification by the linear weighted Kappa statistic. Of the 120 women, 32 (26.7%) were classified as having fatty and 88 (73.3%) as mixed/dense mammographic density, according to Danish dichotomous classification. According to BI-RADS density classification, 12 (10.0%) women were classified as having predominantly fatty (BI-RADS code 1), 46 (38.3%) as having scattered fibroglandular (BI-RADS code 2), 57 (47.5%) as having heterogeneously dense (BI-RADS 3), and five (4.2%) as having extremely dense (BI-RADS code 4) mammographic density. The inter-rater variability assessed by weighted kappa statistic showed a substantial agreement (0.75). The dichotomous mammographic density classification system utilized in early years of Copenhagen's mammographic screening program (1991-2001) agreed well with the BI-RADS density classification system.

Are the determinants of vertebral endplate changes and severe disc degeneration in the lumbar spine the same? A magnetic resonance imaging study in middle-aged male workers.

PubMed

Kuisma, Mari; Karppinen, Jaro; Haapea, Marianne; Niinimäki, Jaakko; Ojala, Risto; Heliövaara, Markku; Korpelainen, Raija; Kaikkonen, Kaisu; Taimela, Simo; Natri, Antero; Tervonen, Osmo

2008-04-16

Modic changes are bone marrow lesions visible in magnetic resonance imaging (MRI), and they are assumed to be associated with symptomatic intervertebral disc disease, especially changes located at L5-S1. Only limited information exists about the determinants of Modic changes. The objective of this study was to evaluate the determinants of vertebral endplate (Modic) changes, and whether they are similar for Modic changes and severe disc degeneration focusing on L5-S1 level. 228 middle-aged male workers (159 train engineers and 69 sedentary factory workers) from northern Finland underwent sagittal T1- and T2-weighted MRI. Modic changes and disc degeneration were analyzed from the scans. The participants responded to a questionnaire including items of occupational history and lifestyle factors. Logistic regression analysis was used to evaluate the associations between selected determinants (age, lifetime exercise, weight-related factors, fat percentage, smoking, alcohol use, lifetime whole-body vibration) and Modic type I and II changes, and severe disc degeneration (= grade V on Pfirrmann's classification). The prevalences of the Modic changes and severe disc degeneration were similar in the occupational groups. Age was significantly associated with all degenerative changes. In the age-adjusted analyses, only weight-related determinants (BMI, waist circumference) were associated with type II changes. Exposure to whole-body vibration, besides age, was the only significant determinant for severe disc degeneration. In the multivariate model, BMI was associated with type II changes at L5-S1 (OR 2.75 per one SD = 3 unit increment in BMI), and vibration exposure with severe disc degeneration at L5-S1 (OR 1.08 per one SD = 11-year increment in vibration exposure). Besides age, weight-related factors seem important in the pathogenesis of Modic changes, whereas whole-body vibration was the only significant determinant of severe disc degeneration.

Are the determinants of vertebral endplate changes and severe disc degeneration in the lumbar spine the same? A magnetic resonance imaging study in middle-aged male workers

PubMed Central

Kuisma, Mari; Karppinen, Jaro; Haapea, Marianne; Niinimäki, Jaakko; Ojala, Risto; Heliövaara, Markku; Korpelainen, Raija; Kaikkonen, Kaisu; Taimela, Simo; Natri, Antero; Tervonen, Osmo

2008-01-01

Background Modic changes are bone marrow lesions visible in magnetic resonance imaging (MRI), and they are assumed to be associated with symptomatic intervertebral disc disease, especially changes located at L5-S1. Only limited information exists about the determinants of Modic changes. The objective of this study was to evaluate the determinants of vertebral endplate (Modic) changes, and whether they are similar for Modic changes and severe disc degeneration focusing on L5-S1 level. Methods 228 middle-aged male workers (159 train engineers and 69 sedentary factory workers) from northern Finland underwent sagittal T1- and T2-weighted MRI. Modic changes and disc degeneration were analyzed from the scans. The participants responded to a questionnaire including items of occupational history and lifestyle factors. Logistic regression analysis was used to evaluate the associations between selected determinants (age, lifetime exercise, weight-related factors, fat percentage, smoking, alcohol use, lifetime whole-body vibration) and Modic type I and II changes, and severe disc degeneration (= grade V on Pfirrmann's classification). Results The prevalences of the Modic changes and severe disc degeneration were similar in the occupational groups. Age was significantly associated with all degenerative changes. In the age-adjusted analyses, only weight-related determinants (BMI, waist circumference) were associated with type II changes. Exposure to whole-body vibration, besides age, was the only significant determinant for severe disc degeneration. In the multivariate model, BMI was associated with type II changes at L5-S1 (OR 2.75 per one SD = 3 unit increment in BMI), and vibration exposure with severe disc degeneration at L5-S1 (OR 1.08 per one SD = 11-year increment in vibration exposure). Conclusion Besides age, weight-related factors seem important in the pathogenesis of Modic changes, whereas whole-body vibration was the only significant determinant of severe disc degeneration. PMID:18416819

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Resampling probability values for weighted kappa with multiple raters.

PubMed

Mielke, Paul W; Berry, Kenneth J; Johnston, Janis E

2008-04-01

A new procedure to compute weighted kappa with multiple raters is described. A resampling procedure to compute approximate probability values for weighted kappa with multiple raters is presented. Applications of weighted kappa are illustrated with an example analysis of classifications by three independent raters.

Designing boosting ensemble of relational fuzzy systems.

PubMed

Scherer, Rafał

2010-10-01

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.

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.

Comparison of weight loss by weight classification in a commercial, community-based weight loss program

USDA-ARS?s Scientific Manuscript database

The objective of our study was to determine the impact of grade of obesity on weight-loss outcomes of a community-based, intensive behavioral counseling program (Weight Watchers Points-Plus). Previous studies have shown that individuals with a higher body mass index (BMI) at the beginning of treatme...

Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures

PubMed Central

Natsoulis, Georges; El Ghaoui, Laurent; Lanckriet, Gert R.G.; Tolley, Alexander M.; Leroy, Fabrice; Dunlea, Shane; Eynon, Barrett P.; Pearson, Cecelia I.; Tugendreich, Stuart; Jarnagin, Kurt

2005-01-01

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as “rewards” for the class-of-interest) while others have a negative contribution (act as “penalties”) to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class. PMID:15867433

Anthropometrically determined nutritional status of urban primary schoolchildren in Makurdi, Nigeria

PubMed Central

2011-01-01

Background No information exists on the nutritional status of primary school children residing in Makurdi, Nigeria. It is envisaged that the data could serve as baseline data for future studies, as well as inform public health policy. The aim of this study was to assess the prevalence of malnutrition among urban school children in Makurdi, Nigeria. Methods Height and weight of 2015 (979 boys and 1036 girls), aged 9-12 years, attending public primary school in Makurdi were measured and the body mass index (BMI) calculated. Anthropometric indices of weight-for-age (WA) and height-for-age (HA) were used to estimate the children's nutritional status. The BMI thinness classification was also calculated. Results Underweight (WAZ < -2) and stunting (HAZ < -2) occurred in 43.4% and 52.7%, respectively. WAZ and HAZ mean scores of the children were -0.91(SD = 0.43) and -0.83 (SD = 0.54), respectively. Boys were more underweight (48.8%) than girls (38.5%), and the difference was statistically significant (p = 0.024; p < 0.05). Conversely, girls tend to be more stunted (56.8%) compared to boys (48.4%) (p = 0.004; p < 0.05). Normal WAZ and HAZ occurred in 54.6% and 44.2% of the children, respectively. Using the 2007 World Health Organisation BMI thinness classification, majority of the children exhibited Grade 1 thinness (77.3%), which was predominant at all ages (9-12 years) in both boys and girls. Gender wise, 79.8% boys and 75.0% girls fall within the Grade I thinness category. Based on the WHO classification, severe malnutrition occurred in 31.3% of the children. Conclusions There is severe malnutrition among the school children living in Makurdi. Most of the children are underweight, stunted and thinned. As such, providing community education on environmental sanitation and personal hygienic practices, proper child rearing, breast-feeding and weaning practices would possibly reverse the trends. PMID:21974827

Galaxy Zoo: quantitative visual morphological classifications for 48 000 galaxies from CANDELS

NASA Astrophysics Data System (ADS)

Simmons, B. D.; Lintott, Chris; Willett, Kyle W.; Masters, Karen L.; Kartaltepe, Jeyhan S.; Häußler, Boris; Kaviraj, Sugata; Krawczyk, Coleman; Kruk, S. J.; McIntosh, Daniel H.; Smethurst, R. J.; Nichol, Robert C.; Scarlata, Claudia; Schawinski, Kevin; Conselice, Christopher J.; Almaini, Omar; Ferguson, Henry C.; Fortson, Lucy; Hartley, William; Kocevski, Dale; Koekemoer, Anton M.; Mortlock, Alice; Newman, Jeffrey A.; Bamford, Steven P.; Grogin, N. A.; Lucas, Ray A.; Hathi, Nimish P.; McGrath, Elizabeth; Peth, Michael; Pforr, Janine; Rizer, Zachary; Wuyts, Stijn; Barro, Guillermo; Bell, Eric F.; Castellano, Marco; Dahlen, Tomas; Dekel, Avishai; Ownsworth, Jamie; Faber, Sandra M.; Finkelstein, Steven L.; Fontana, Adriano; Galametz, Audrey; Grützbauch, Ruth; Koo, David; Lotz, Jennifer; Mobasher, Bahram; Mozena, Mark; Salvato, Mara; Wiklind, Tommy

2017-02-01

We present quantified visual morphologies of approximately 48 000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in the Galaxy Zoo project. 90 per cent of galaxies have z ≤ 3 and are observed in rest-frame optical wavelengths by CANDELS. Each galaxy received an average of 40 independent classifications, which we combine into detailed morphological information on galaxy features such as clumpiness, bar instabilities, spiral structure, and merger and tidal signatures. We apply a consensus-based classifier weighting method that preserves classifier independence while effectively down-weighting significantly outlying classifications. After analysing the effect of varying image depth on reported classifications, we also provide depth-corrected classifications which both preserve the information in the deepest observations and also enable the use of classifications at comparable depths across the full survey. Comparing the Galaxy Zoo classifications to previous classifications of the same galaxies shows very good agreement; for some applications, the high number of independent classifications provided by Galaxy Zoo provides an advantage in selecting galaxies with a particular morphological profile, while in others the combination of Galaxy Zoo with other classifications is a more promising approach than using any one method alone. We combine the Galaxy Zoo classifications of `smooth' galaxies with parametric morphologies to select a sample of featureless discs at 1 ≤ z ≤ 3, which may represent a dynamically warmer progenitor population to the settled disc galaxies seen at later epochs.

Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

PubMed

Mu, Guangyu; Liu, Ying; Wang, Limin

2015-01-01

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

Benign-malignant mass classification in mammogram using edge weighted local texture features

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree

2016-03-01

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.

Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

NASA Astrophysics Data System (ADS)

Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

2009-02-01

Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

ERIC Educational Resources Information Center

Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

2006-01-01

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

[Bronchopulmonary dysplasia: definitions and classifications].

PubMed

Sánchez Luna, M; Moreno Hernando, J; Botet Mussons, F; Fernández Lorenzo, J R; Herranz Carrillo, G; Rite Gracia, S; Salguero García, E; Echaniz Urcelay, I

2013-10-01

Bronchopulmonary dysplasia is the most common sequelae related to very low birth weight infants, mostly with those of extremely low birth weight. Even with advances in prevention and treatment of respiratory distress syndrome associated with prematurity, there is still no decrease in the incidence in this population, although a change in its clinical expression and severity has been observed. There are, however, differences in its frequency between health centres, probably due to a non-homogeneously used clinical definition. In this article, the Committee of Standards of the Spanish Society of Neonatology wishes to review the current diagnosis criteria of bronchopulmonary dysplasia to reduce, as much as possible, these inter-centre differences. Copyright © 2013 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.

Credit Risk Evaluation Using a C-Variable Least Squares Support Vector Classification Model

NASA Astrophysics Data System (ADS)

Yu, Lean; Wang, Shouyang; Lai, K. K.

Credit risk evaluation is one of the most important issues in financial risk management. In this paper, a C-variable least squares support vector classification (C-VLSSVC) model is proposed for credit risk analysis. The main idea of this model is based on the prior knowledge that different classes may have different importance for modeling and more weights should be given to those classes with more importance. The C-VLSSVC model can be constructed by a simple modification of the regularization parameter in LSSVC, whereby more weights are given to the lease squares classification errors with important classes than the lease squares classification errors with unimportant classes while keeping the regularized terms in its original form. For illustration purpose, a real-world credit dataset is used to test the effectiveness of the C-VLSSVC model.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. PMID:27079534

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

Building confidence and credibility into CAD with belief decision trees

NASA Astrophysics Data System (ADS)

Affenit, Rachael N.; Barns, Erik R.; Furst, Jacob D.; Rasin, Alexander; Raicu, Daniela S.

2017-03-01

Creating classifiers for computer-aided diagnosis in the absence of ground truth is a challenging problem. Using experts' opinions as reference truth is difficult because the variability in the experts' interpretations introduces uncertainty in the labeled diagnostic data. This uncertainty translates into noise, which can significantly affect the performance of any classifier on test data. To address this problem, we propose a new label set weighting approach to combine the experts' interpretations and their variability, as well as a selective iterative classification (SIC) approach that is based on conformal prediction. Using the NIH/NCI Lung Image Database Consortium (LIDC) dataset in which four radiologists interpreted the lung nodule characteristics, including the degree of malignancy, we illustrate the benefits of the proposed approach. Our results show that the proposed 2-label-weighted approach significantly outperforms the accuracy of the original 5- label and 2-label-unweighted classification approaches by 39.9% and 7.6%, respectively. We also found that the weighted 2-label models produce higher skewness values by 1.05 and 0.61 for non-SIC and SIC respectively on root mean square error (RMSE) distributions. When each approach was combined with selective iterative classification, this further improved the accuracy of classification for the 2-weighted-label by 7.5% over the original, and improved the skewness of the 5-label and 2-unweighted-label by 0.22 and 0.44 respectively.

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease

PubMed Central

Zhan, Liang; Zhou, Jiayu; Wang, Yalin; Jin, Yan; Jahanshad, Neda; Prasad, Gautam; Nir, Talia M.; Leonardo, Cassandra D.; Ye, Jieping; Thompson, Paul M.; for the Alzheimer’s Disease Neuroimaging Initiative

2015-01-01

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods – four of them tensor-based deterministic (FACT, RK2, SL, and TL), two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one “ball-and-stick” approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification. PMID:25926791

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1993-01-01

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Potential Analysis of Rainfall-induced Sediment Disaster

NASA Astrophysics Data System (ADS)

Chen, Jing-Wen; Chen, Yie-Ruey; Hsieh, Shun-Chieh; Tsai, Kuang-Jung; Chue, Yung-Sheng

2014-05-01

Most of the mountain regions in Taiwan are sedimentary and metamorphic rocks which are fragile and highly weathered. Severe erosion occurs due to intensive rainfall and rapid flow, the erosion is even worsen by frequent earthquakes and severely affects the stability of hillsides. Rivers are short and steep in Taiwan with large runoff differences in wet and dry seasons. Discharges respond rapidly with rainfall intensity and flood flows usually carry large amount of sediment. Because of the highly growth in economics and social change, the development in the slope land is inevitable in Taiwan. However, sediment disasters occur frequently in high and precipitous region during typhoon. To make the execution of the regulation of slope land development more efficiency, construction of evaluation model for sediment potential is very important. In this study, the Genetic Adaptive Neural Network (GANN) was implemented in texture analysis techniques for the classification of satellite images of research region before and after typhoon or extreme rainfall and to obtain surface information and hazard log data. By using GANN weight analysis, factors, levels and probabilities of disaster of the research areas are presented. Then, through geographic information system the disaster potential map is plotted to distinguish high potential regions from low potential regions. Finally, the evaluation processes for sediment disaster after rainfall due to slope land use are established. In this research, the automatic image classification and evaluation modules for sediment disaster after rainfall due to slope land disturbance and natural environment are established in MATLAB to avoid complexity and time of computation. After implementation of texture analysis techniques, the results show that the values of overall accuracy and coefficient of agreement of the time-saving image classification for different time periods are at intermediate-high level and above. The results of GANN show that the weight of building density is the largest in all slope land disturbance factors, followed by road density, orchard density, baren land density, vegetation density, and farmland density. The weight of geology is the largest in all natural environment factors, followed by slope roughness, slope, and elevation. Overlaying the locations of large sediment disaster in the past on the potential map predicted by GANN, we found that most damage areas were in the region with medium-high or high potential of landslide. Therefore, the proposed potential model of sediment disaster can be used in practice.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Predictors of death from severe pneumonia among children 2-59 months old hospitalized in Bohol, Philippines: implications for referral criteria at a first-level health facility.

PubMed

Lupisan, S P; Ruutu, P; Erma Abucejo-Ladesma, P; Quiambao, B P; Gozum, L; Sombrero, L T; Romano, V; Herva, E; Riley, I; Simoes, E A F

2007-08-01

To determine predictors of death among children 2-59 months old admitted to hospital with severe pneumonia. Prospective observational study from April 1994 to May 2000 to investigate serious infections in children less than 5 years old admitted to a tertiary care government hospital in a rural province in central Philippines. The quality of clinical and laboratory work was monitored. The WHO classification for severe pneumonia was used for patient enrolment. There were 1249 children with severe pneumonia and no CNS infection. Thirty children died. Using univariate analysis, the following factors were significantly associated with death: age 2-5 months, dense infiltrates on chest radiography and presence of definite bacterial pathogens in the blood. Stepwise logistic regression analysis revealed the following independent predictors of death: age 2-5 months, weight for age z-score less than -2 SD, dense infiltrates on chest radiography and definite pathogens isolated in the blood. When the results of chest radiographs and blood cultures were not included to mimic facilities available at first-level facilities, age 2-5 months and weight for age z-score less than -2 SD remained independent predictors of death. When resources are limited, children with lower chest wall indrawing (severe pneumonia) who are 2-5 months old or moderately to severely malnourished should be referred for immediate higher-level care.

Stand up time in tunnel base on rock mass rating Bieniawski 1989

NASA Astrophysics Data System (ADS)

Nata, Refky Adi; M. S., Murad

2017-11-01

RMR (Rock Mass Rating), or also known as the geo mechanics classification has been modified and made as the International Standard in determination of rock mass weighting. Rock Mass Rating Classification has been developed by Bieniawski (since 1973, 1976, and 1989). The goals of this research are investigate the class of rocks base on classification rock mass rating Bieniawski 1989, to investigate the long mass of the establishment rocks, and also to investigate the distance of the opening tunnel without a support especially in underground mine. On the research measuring: strength intact rock material, RQD (Rock Quality Designation), spacing of discontinuities, condition of discontinuities, groundwater, and also adjustment for discontinuity orientations. On testing samples in the laboratory for coal obtained strong press UCS of 30.583 MPa. Based on the classification according to Bieniawski has a weight of 4. As for silt stone obtained strong press of 35.749 MPa, gained weight also by 4. From the results of the measurements obtained for coal RQD value average 97.38 %, so it has a weight of 20. While in siltstone RQD value average 90.10 % so it has weight 20 also. On the coal the average distance measured in field is 22.6 cm so as to obtain a weight of 10, while for siltstone has an average is 148 cm, so it has weight = 15. Presistence in the field vary, on coal = 57.28 cm, so it has weight is 6 and persistence on siltstone 47 cm then does it weight to 6. Base on table Rock Mass Rating according to Bieniawski 1989, aperture on coal = 0.41 mm. That is located in the range 0.1-1 mm, so it has weight is 4. Besides that, for the siltstone aperture = 21.43 mm. That is located in the range > 5 mm, so the weight = 0. Roughness condition in coal and siltstone classified into rough so it has weight 5. Infilling condition in coal and siltstone classified into none so it has weight 6. Weathering condition in coal and siltstone classified into highly weathered so it has weight 1. Groundwater condition in coal classified into dripping so it has weight 4. and siltstone classified into completely dry so it has weight 15. Discontinuity orientation in coal parallel axis of the tunnel. The range is 251°-290° so unfavorable. It has weight = -10. In siltstone also discontinuity parallel axis of the tunnel. The range located between 241°-300°. Base on weighting parameters according to Bieniawski 1989, concluded for rocks are there in class II with value is 62, and able to establishment until 6 months. For the distance of the opening tunnel without a support as far as 8 meters.

Optimization Of Feature Weight TheVoting Feature Intervals 5 Algorithm Using Partical Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril

2017-12-01

The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.

Automated Grouping of Opportunity Rover Alpha Particle X-Ray Spectrometer Compositional Data

NASA Technical Reports Server (NTRS)

VanBommel, S. J.; Gellert, R.; Clark, B. C.; Ming, D. W.; Mittlefehldt, D. W.; Schroder, C.; Yen, A. S.

2016-01-01

The Alpha Particle X-ray Spectrometer (APXS) conducts high-precision in situ measurements of rocks and soils on both active NASA Mars rovers. Since 2004 the rover Opportunity has acquired around 440 unique APXS measurements, including a wide variety of compositions, during its 42+ kilometers traverse across several geological formations. Here we discuss an analytical comparison algorithm providing a means to cluster samples due to compositional similarity and the resulting automated classification scheme. Due to the inherent variance of elements in the APXS data set, each element has an associated weight that is inversely proportional to the variance. Thus, the more consistent the abundance of an element in the data set, the more it contributes to the classification. All 16 elements standard to the APXS data set are considered. Careful attention is also given to the errors associated with the composition measured by the APXS - larger uncertainties reduce the weighting of the element accordingly. The comparison of two targets, i and j, generates a similarity score, S(sub ij). This score is immediately comparable to an average ratio across all elements if one assumes standard weighted uncertainty. The algorithm facilitates the classification of APXS targets by chemistry alone - independent of target appearance and geological context which can be added later as a consistency check. For the N targets considered, a N by N hollow matrix, S, is generated where S = S(sup T). The average relation score, S(sub av), for target N(sub i) is simply the average of column i of S. A large S(sub av) is indicative of a unique sample. In such an instance any targets with a low comparison score can be classified alike. The threshold between classes requires careful consideration. Applying the algorithm to recent Marathon Valley targets indicates similarities with Burns formation and average-Mars-like rocks encountered earlier at Endeavour Crater as well as a new class of felsic rocks.

Child obesity cut-offs as derived from parental perceptions: cross-sectional questionnaire.

PubMed

Black, James A; Park, MinHae; Gregson, John; Falconer, Catherine L; White, Billy; Kessel, Anthony S; Saxena, Sonia; Viner, Russell M; Kinra, Sanjay

2015-04-01

Overweight children are at an increased risk of premature mortality and disease in adulthood. Parental perceptions and clinical definitions of child obesity differ, which may lessen the effectiveness of interventions to address obesity in the home setting. The extent to which parental and objective weight status cut-offs diverge has not been documented. To compare parental perceived and objectively derived assessment of underweight, healthy weight, and overweight in English children, and to identify sociodemographic characteristics that predict parental under- or overestimation of a child's weight status. Cross-sectional questionnaire completed by parents linked with objective measurement of height and weight by school nurses, in English children from five regions aged 4-5 and 10-11 years old. Parental derived cut-offs for under- and overweight were derived from a multinomial model of parental classification of their own child's weight status against school nurse measured body mass index (BMI) centile. Measured BMI centile was matched with parent classification of weight status in 2976 children. Parents become more likely to classify their children as underweight when they are at the 0.8th centile or below, and overweight at the 99.7th centile or above. Parents were more likely to underestimate a child's weight if the child was black or South Asian, male, more deprived, or the child was older. These values differ greatly from the BMI centile cut-offs for underweight (2nd centile) and overweight (85th). Clinical and parental classifications of obesity are divergent at extremes of the weight spectrum. © British Journal of General Practice 2015.

Disability weights based on patient-reported data from a multinational injury cohort

PubMed Central

Lyons, Ronan A; Simpson, Pamela M; Rivara, Frederick P; Ameratunga, Shanthi; Polinder, Suzanne; Derrett, Sarah; Harrison, James E

2016-01-01

Abstract Objective To create patient-based disability weights for individual injury diagnosis codes and nature-of-injury classifications, for use, as an alternative to panel-based weights, in studies on the burden of disease. Methods Self-reported data based on the EQ-5D standardized measure of health status were collected from 29 770 participants in the Injury-VIBES injury cohort study, which covered Australia, the Netherlands, New Zealand, the United Kingdom of Great Britain and Northern Ireland and the United States of America. The data were combined to calculate new disability weights for each common injury classification and for each type of diagnosis covered by the 10th revision of the International statistical classification of diseases and related health problems. Weights were calculated separately for hospital admissions and presentations confined to emergency departments. Findings There were 29 770 injury cases with at least one EQ-5D score. The mean age of the participants providing data was 51 years. Most participants were male and almost a third had road traffic injuries. The new disability weights were higher for admitted cases than for cases confined to emergency departments and higher than the corresponding weights used by the Global Burden of Disease 2013 study. Long-term disability was common in most categories of injuries. Conclusion Injury is often a chronic disorder and burden of disease estimates should reflect this. Application of the new weights to burden studies would substantially increase estimates of disability-adjusted life-years and provide a more accurate reflection of the impact of injuries on peoples’ lives. PMID:27821883

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

PubMed

Pasquier, C; Promponas, V J; Hamodrakas, S J

2001-08-15

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

12 CFR 702.105 - Weighted-average life of investments.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 6 2011-01-01 2011-01-01 false Weighted-average life of investments. 702.105... PROMPT CORRECTIVE ACTION Net Worth Classification § 702.105 Weighted-average life of investments. Except as provided below (Table 3), the weighted-average life of an investment for purposes of §§ 702.106(c...

12 CFR 702.105 - Weighted-average life of investments.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Weighted-average life of investments. 702.105... PROMPT CORRECTIVE ACTION Net Worth Classification § 702.105 Weighted-average life of investments. Except as provided below (Table 3), the weighted-average life of an investment for purposes of §§ 702.106(c...

12 CFR 702.105 - Weighted-average life of investments.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 7 2014-01-01 2014-01-01 false Weighted-average life of investments. 702.105... PROMPT CORRECTIVE ACTION Net Worth Classification § 702.105 Weighted-average life of investments. Except as provided below (Table 3), the weighted-average life of an investment for purposes of §§ 702.106(c...

12 CFR 702.105 - Weighted-average life of investments.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 7 2013-01-01 2013-01-01 false Weighted-average life of investments. 702.105... PROMPT CORRECTIVE ACTION Net Worth Classification § 702.105 Weighted-average life of investments. Except as provided below (Table 3), the weighted-average life of an investment for purposes of §§ 702.106(c...

12 CFR 702.105 - Weighted-average life of investments.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 7 2012-01-01 2012-01-01 false Weighted-average life of investments. 702.105... PROMPT CORRECTIVE ACTION Net Worth Classification § 702.105 Weighted-average life of investments. Except as provided below (Table 3), the weighted-average life of an investment for purposes of §§ 702.106(c...

Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches

NASA Astrophysics Data System (ADS)

Lee, Haeil; Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

Lung cancer is the most common cause of cancer-related death. To diagnose lung cancers in early stages, numerous studies and approaches have been developed for cancer screening with computed tomography (CT) imaging. In recent years, convolutional neural networks (CNN) have become one of the most common and reliable techniques in computer aided detection (CADe) and diagnosis (CADx) by achieving state-of-the-art-level performances for various tasks. In this study, we propose a CNN classification system for false positive reduction of initially detected lung nodule candidates. First, image patches of lung nodule candidates are extracted from CT scans to train a CNN classifier. To reflect the volumetric contextual information of lung nodules to 2D image patch, we propose a weighted average image patch (WAIP) generation by averaging multiple slice images of lung nodule candidates. Moreover, to emphasize central slices of lung nodules, slice images are locally weighted according to Gaussian distribution and averaged to generate the 2D WAIP. With these extracted patches, 2D CNN is trained to achieve the classification of WAIPs of lung nodule candidates into positive and negative labels. We used LUNA 2016 public challenge database to validate the performance of our approach for false positive reduction in lung CT nodule classification. Experiments show our approach improves the classification accuracy of lung nodules compared to the baseline 2D CNN with patches from single slice image.

Comparing the usefulness of the 1997 and 2009 WHO dengue case classification: a systematic literature review.

PubMed

Horstick, Olaf; Jaenisch, Thomas; Martinez, Eric; Kroeger, Axel; See, Lucy Lum Chai; Farrar, Jeremy; Ranzinger, Silvia Runge

2014-09-01

The 1997 and 2009 WHO dengue case classifications were compared in a systematic review with 12 eligible studies (4 prospective). Ten expert opinion articles were used for discussion. For the 2009 WHO classification studies show: when determining severe dengue sensitivity ranges between 59-98% (88%/98%: prospective studies), specificity between 41-99% (99%: prospective study) - comparing the 1997 WHO classification: sensitivity 24.8-89.9% (24.8%/74%: prospective studies), specificity: 25%/100% (100%: prospective study). The application of the 2009 WHO classification is easy, however for (non-severe) dengue there may be a risk of monitoring increased case numbers. Warning signs validation studies are needed. For epidemiological/pathogenesis research use of the 2009 WHO classification, opinion papers show that ease of application, increased sensitivity (severe dengue) and international comparability are advantageous; 3 severe dengue criteria (severe plasma leakage, severe bleeding, severe organ manifestation) are useful research endpoints. The 2009 WHO classification has clear advantages for clinical use, use in epidemiology is promising and research use may at least not be a disadvantage. © The American Society of Tropical Medicine and Hygiene.

EULAR/ACR classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups: a methodology report

PubMed Central

Bottai, Matteo; Tjärnlund, Anna; Santoni, Giola; Werth, Victoria P; Pilkington, Clarissa; de Visser, Marianne; Alfredsson, Lars; Amato, Anthony A; Barohn, Richard J; Liang, Matthew H; Aggarwal, Rohit; Arnardottir, Snjolaug; Chinoy, Hector; Cooper, Robert G; Danko, Katalin; Dimachkie, Mazen M; Feldman, Brian M; García-De La Torre, Ignacio; Gordon, Patrick; Hayashi, Taichi; Katz, James D; Kohsaka, Hitoshi; Lachenbruch, Peter A; Lang, Bianca A; Li, Yuhui; Oddis, Chester V; Olesinka, Marzena; Reed, Ann M; Rutkowska-Sak, Lidia; Sanner, Helga; Selva-O’Callaghan, Albert; Wook Song, Yeong; Ytterberg, Steven R; Miller, Frederick W; Rider, Lisa G; Lundberg, Ingrid E; Amoruso, Maria

2017-01-01

Objective To describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups. Methods An international, multidisciplinary group of myositis experts produced a set of 93 potentially relevant variables to be tested for inclusion in the criteria. Rheumatology, dermatology, neurology and paediatric clinics worldwide collected data on 976 IIM cases (74% adults, 26% children) and 624 non-IIM comparator cases with mimicking conditions (82% adults, 18% children). The participating clinicians classified each case as IIM or non-IIM. Generally, the classification of any given patient was based on few variables, leaving remaining variables unmeasured. We investigated the strength of the association between all variables and between these and the disease status as determined by the physician. We considered three approaches: (1) a probability-score approach, (2) a sum-of-items approach criteria and (3) a classification-tree approach. Results The approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probability-score approach showed superior statistical performance and clinical practicability and was therefore preferred over the others. We developed a classification tree for subclassification of patients with IIM. A calculator for electronic devices, such as computers and smartphones, facilitates the use of the European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria. Conclusions The new EULAR/ACR classification criteria provide a patient’s probability of having IIM for use in clinical and research settings. The probability is based on a score obtained by summing the weights associated with a set of criteria items. PMID:29177080

Assessment of incidence of severe sepsis in Sweden using different ways of abstracting International Classification of Diseases codes: difficulties with methods and interpretation of results.

PubMed

Wilhelms, Susanne B; Huss, Fredrik R; Granath, Göran; Sjöberg, Folke

2010-06-01

To compare three International Classification of Diseases code abstraction strategies that have previously been reported to mirror severe sepsis by examining retrospective Swedish national data from 1987 to 2005 inclusive. Retrospective cohort study. Swedish hospital discharge database. All hospital admissions during the period 1987 to 2005 were extracted and these patients were screened for severe sepsis using the three International Classification of Diseases code abstraction strategies, which were adapted for the Swedish version of the International Classification of Diseases. Two code abstraction strategies included both International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision codes, whereas one included International Classification of Diseases, Tenth Revision codes alone. None. The three International Classification of Diseases code abstraction strategies identified 37,990, 27,655, and 12,512 patients, respectively, with severe sepsis. The incidence increased over the years, reaching 0.35 per 1000, 0.43 per 1000, and 0.13 per 1000 inhabitants, respectively. During the International Classification of Diseases, Ninth Revision period, we found 17,096 unique patients and of these, only 2789 patients (16%) met two of the code abstraction strategy lists and 14,307 (84%) met one list. The International Classification of Diseases, Tenth Revision period included 46,979 unique patients, of whom 8% met the criteria of all three International Classification of Diseases code abstraction strategies, 7% met two, and 84% met one only. The three different International Classification of Diseases code abstraction strategies generated three almost separate cohorts of patients with severe sepsis. Thus, the International Classification of Diseases code abstraction strategies for recording severe sepsis in use today provides an unsatisfactory way of estimating the true incidence of severe sepsis. Further studies relating International Classification of Diseases code abstraction strategies to the American College of Chest Physicians/Society of Critical Care Medicine scores are needed.

Obesity Differentially Affects Phenotypes of Polycystic Ovary Syndrome

PubMed Central

Moran, Carlos; Arriaga, Monica; Rodriguez, Gustavo; Moran, Segundo

2012-01-01

Obesity or overweight affect most of patients with polycystic ovary syndrome (PCOS). Phenotypes are the clinical characteristics produced by the interaction of heredity and environment in a disease or syndrome. Phenotypes of PCOS have been described on the presence of clinical hyperandrogenism, oligoovulation and polycystic ovaries. The insulin resistance is present in the majority of patients with obesity and/or PCOS and it is more frequent and of greater magnitude in obese than in non obese PCOS patients. Levels of sexual hormone binding globulin are decreased, and levels of free androgens are increased in obese PCOS patients. Weight loss treatment is important for overweight or obese PCOS patients, but not necessary for normal weight PCOS patients, who only need to avoid increasing their body weight. Obesity decreases or delays several infertility treatments. The differences in the hormonal and metabolic profile, as well as the different focus and response to treatment between obese and non obese PCOS patients suggest that obesity has to be considered as a characteristic for classification of PCOS phenotypes. PMID:22829818

Mild Depressive Symptoms Among Americans in Relation to Physical Activity, Current Overweight/Obesity, and Self-Reported History of Overweight/Obesity.

PubMed

Dankel, Scott J; Loenneke, Jeremy P; Loprinzi, Paul D

2016-10-01

Overweight/obese individuals are at an increased risk for depression with some evidence of a bidirectional association. The preventative effects of physical activity among overweight/obese individuals have been well documented; however, less is known on how the duration of overweight/obesity alters the association with negative health outcomes. Therefore, the purpose of this investigation was to determine how the classification, and more specifically duration, of overweight/obesity alters the association between physical activity and depressive symptoms. The 2005-2006 National Health and Nutrition Examination Survey (NHANES) data were used (n = 764), and individuals were divided into six mutually exclusive groups based on physical activity status, weight classification (measured BMI), and duration of weight classification (assessed via recall). Multivariable linear and logistic regression analyses were computed to examine odds of depressive symptoms (patient health questionnaire (PHQ)-9) among groups. After adjusting for covariates, only individuals who were inactive and overweight/obese at the examination and 10 years prior were at an increased odds of depressive symptoms in comparison to those who were active and normal weight (odds ratio (OR) = 2.40; 95 % confidence interval (CI) 1.03, 5.61; p = 0.04). Physical activity appeared to ameliorate the association with depressive symptoms independent of overweight/obesity classification or duration. The cyclic nature of overweight/obesity and depression (i.e., bidirectional association) appears to increase the odds of depression as the length of overweight/obesity is increased. These results provide support for clinicians to assess not only their clients' current BMI but also the duration in which they have been at a certain weight classification and to further promote physical activity as a preventative measure against depressive symptoms.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

26 CFR 48.4071-2 - Determination of weight.

Code of Federal Regulations, 2010 CFR

2010-04-01

... EXCISE TAXES MANUFACTURERS AND RETAILERS EXCISE TAXES Motor Vehicles, Tires, Tubes, Tread Rubber, and... each type, size, grade, and classification. The average weights must be established in accordance with...

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.

A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Fusion and Sense Making of Heterogeneous Sensor Network and Other Sources

DTIC Science & Technology

2017-03-16

multimodal fusion framework that uses both training data and web resources for scene classification, the experimental results on the benchmark datasets...show that the proposed text-aided scene classification framework could significantly improve classification performance. Experimental results also show...human whose adaptability is achieved by reliability- dependent weighting of different sensory modalities. Experimental results show that the proposed

Improvement of an algorithm for recognition of liveness using perspiration in fingerprint devices

NASA Astrophysics Data System (ADS)

Parthasaradhi, Sujan T.; Derakhshani, Reza; Hornak, Lawrence A.; Schuckers, Stephanie C.

2004-08-01

Previous work in our laboratory and others have demonstrated that spoof fingers made of a variety of materials including silicon, Play-Doh, clay, and gelatin (gummy finger) can be scanned and verified when compared to a live enrolled finger. Liveness, i.e. to determine whether the introduced biometric is coming from a live source, has been suggested as a means to circumvent attacks using spoof fingers. We developed a new liveness method based on perspiration changes in the fingerprint image. Recent results showed approximately 90% classification rate using different classification methods for various technologies including optical, electro-optical, and capacitive DC, a shorter time window and a diverse dataset. This paper focuses on improvement of the live classification rate by using a weight decay method during the training phase in order to improve the generalization and reduce the variance of the neural network based classifier. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental impression material and Play-Doh, and fourteen cadaver fingers. 100% live classification was achieved with 81.8 to 100% spoof classification, depending on the device technology. The weight-decay method improves upon past reports by increasing the live and spoof classification rate.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Use of routinely available clinical, nutritional, and functional criteria to classify cachexia in advanced cancer patients.

PubMed

Vigano, Antonio A L; Morais, José A; Ciutto, Lorella; Rosenthall, Leonard; di Tomasso, Jonathan; Khan, Sarah; Olders, Henry; Borod, Manuel; Kilgour, Robert D

2017-10-01

Cachexia is a highly prevalent syndrome in cancer and chronic diseases. However, due to the heterogeneous features of cancer cachexia, its identification and classification challenge clinical practitioners. To determine the clinical relevance of a cancer cachexia classification system in advanced cancer patients. Beginning with the four-stage classification system proposed for cachexia [non-cachexia (NCa), pre-cachexia (PCa), cachexia (Ca) and refractory cachexia (RCa)], we assigned patients to these cachexia stages according to five classification criteria available in clinical practice: 1) biochemistry (high C-reactive protein or leukocytes, or hypoalbuminemia, or anemia), 2) food intake (normal/decreased), weight loss: 3) moderate (≤5%) or 4) significant (>5%/past six months) and 5) performance status (Eastern Cooperative Oncology Group Performance Status ≥ 3). We then determined if symptom severity, body composition changes, functional levels, hospitalizations and survival rates varied significantly across cachexia stages. Two-hundred and ninety-seven advanced cancer patients with primary gastrointestinal and lung tumors were included. Patients were classified into Ca (36%), PCa and RCa (21%, respectively) and NCa (15%). Significant (p < 0.05) differences were observed among cachexia stages for most of the outcome measures (symptoms, body composition, handgrip strength, emergency room visits and length of hospital stays) according to cachexia severity. Survival also differed between cachexia stages (except between PCa and Ca). Five clinical criteria can be used to stage cancer cachexia patients and predict important clinical, nutritional and functional outcomes. The lack of statistical difference between PCa and Ca in almost all clinical outcomes examined suggests either that the PCa group includes patients already affected by early cachexia or that more precise criteria are needed to differentiate PCa from Ca patients. More studies are required to validate these findings. Copyright © 2016. Published by Elsevier Ltd.

Framework for evaluating disease severity measures in older adults with comorbidity.

PubMed

Boyd, Cynthia M; Weiss, Carlos O; Halter, Jeff; Han, K Carol; Ershler, William B; Fried, Linda P

2007-03-01

Accounting for the influence of concurrent conditions on health and functional status for both research and clinical decision-making purposes is especially important in older adults. Although approaches to classifying severity of individual diseases and conditions have been developed, the utility of these classification systems has not been evaluated in the presence of multiple conditions. We present a framework for evaluating severity classification systems for common chronic diseases. The framework evaluates the: (a) goal or purpose of the classification system; (b) physiological and/or functional criteria for severity graduation; and (c) potential reliability and validity of the system balanced against burden and costs associated with classification. Approaches to severity classification of individual diseases were not originally conceived for the study of comorbidity. Therefore, they vary greatly in terms of objectives, physiological systems covered, level of severity characterization, reliability and validity, and costs and burdens. Using different severity classification systems to account for differing levels of disease severity in a patient with multiple diseases, or, assessing global disease burden may be challenging. Most approaches to severity classification are not adequate to address comorbidity. Nevertheless, thoughtful use of some existing approaches and refinement of others may advance the study of comorbidity and diagnostic and therapeutic approaches to patients with multimorbidity.

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.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

PubMed Central

Gupta, Arpana; Mayer, Emeran A.; Sanmiguel, Claudia P.; Van Horn, John D.; Woodworth, Davis; Ellingson, Benjamin M.; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S.

2015-01-01

Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. Conclusions 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity. PMID:25737959

Comparison of Mid-Upper Arm Circumference and Weight-for-Height to Diagnose Severe Acute Malnutrition: A Study in Southern Ethiopia

PubMed Central

Tadesse, Amare Worku; Tadesse, Elazar; Berhane, Yemane; Ekström, Eva-Charlotte

2017-01-01

Weight-for-height Z-score (WHZ) and mid-upper arm circumference (MUAC) are two independent anthropometric indicators for diagnosing and admitting children with severe acute malnutrition (SAM) for treatment. While severely wasted children are at high risk of mortality, MUAC and WHZ do not always identify the same population of children as having SAM. Understanding how this discrepancy relates to age and sex may provide valuable information for care programmes for children with SAM. Age and sex distribution for differences between children identified as SAM by MUAC and WHZ were examined and the degree of agreement calculated. Children (n = 4297) aged 6–59 months with validated anthropometric measures were recruited from a population-based survey conducted in rural southern Ethiopia. MUAC < 115 mm and WHZ < −3 were used to define severe wasting as per the World Health Organization (WHO) classification. The kappa coefficient (κ) was calculated. There was fair agreement between the MUAC and WHZ definitions of severe wasting in boys (κ = 0.37) and children younger than 24 months (κ = 0.32) but poor agreement in girls (κ = 0.15) and children aged 24 months and above (κ = 0.13). More research is needed on response to treatment and prediction of mortality using different anthropometric measurements in relation to ages and sex of children. PMID:28287482

42 CFR 416.167 - Basis of payment.

Code of Federal Regulations, 2010 CFR

2010-10-01

... classification (APC) groups and payment weights. (1) ASC covered surgical procedures are classified using the APC... section, an ASC relative payment weight is determined based on the APC relative payment weight for each covered surgical procedure and covered ancillary service that has an applicable APC relative payment...

Modified Treatment Algorithm for Pseudogynecomastia After Massive Weight Loss.

PubMed

Ziegler, Ulrich E; Lorenz, Udo; Daigeler, Adrien; Ziegler, Selina N; Zeplin, Philip H

2018-06-19

Pseudogynecomastia is the increased aggregation of fatty tissue in the area of the male breast with resultant female appearance. Two forms can appear: pseudogynecomastia after massive weight loss (pseudogynecomastia obese [PO]) and pseudogynecomastia, which is caused only by adipose tissue (pseudogynecomastia fat). For PO, only the Gusenoff classification with corresponding operative treatment options exists. However, this classification is limited by the fact that it underestimates the extensive variability of residual fat tissue and skin excess, both crucial factors for operative planning. For this reason, we propose a modification of the treatment algorithm for the Gusenoff classification based on our results to achieve more masculine results. A total of 43 male patients with PO were included in this retrospective study (grade 1a, n = 1; grade 1b, n = 1; grade 2, n = 17; grade 3, n = 24). Forty-two mastectomies with a free nipple-areola complex (NAC) transposition (grades 2 and 3) and 1 with a subcutaneous mastectomy (grade 1a) with periareolar lifting were performed. A retrospective chart review was performed to obtain data regarding age, body mass index, body mass index loss, weight loss, reason for weight loss, comorbidities, nicotine, and additional procedures, postoperative sensitive on the NAC transplants and complications. None of the free-nipple grafts were lost. Forty (95%) of 42 patients with mastectomy had a resensitivity on the NAC. For pseudogynecomastia, the treatment algorithm of the Gusenoff classification should be modified and adapted according to our recommendations to achieve more optimal masculine results.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

21 CFR 876.5365 - Esophageal dilator.

Code of Federal Regulations, 2010 CFR

2010-04-01

... and weighted with mercury or a metal olive-shaped weight that slides on a guide, such as a string or... esophageal or gastrointestinal bougies and the esophageal dilator (metal olive). (b) Classification. Class II...

Perceived size of friends and weight evaluation among low-income adolescents

PubMed Central

Milan, Stephanie

2018-01-01

Drawing from social comparison theory, we examine how perceptions of friends’ body sizes may influence adolescents’ subjective evaluations of their own body (e.g., how accurate they are in judging their weight, how much body dissatisfaction they feel), particularly for adolescent females. Participants were low-income, minority adolescents (Study 1: N = 194 females, Mean age = 15.4; Study 2: N = 409 males and females; Mean age = 14.9). Adolescents used figure rating scales to indicate their perceived size and that of four of their closest friends and completed several measures of subjective weight evaluation (e.g., weight classification, body dissatisfaction, internalized weight bias). In both studies, how adolescents perceived their body size and the body sizes of their thinnest and heaviest friends were positively correlated. In Study 1, overweight females based on measured BMI were less likely to accurately judge themselves as overweight if they had a close friend they perceived as heavy. In addition, females who viewed themselves as having a larger figure reported more internalized weight bias when they had friends they viewed as relatively thin. Findings from Study 2 suggest that how friends’ bodies are perceived is predictive of subjective weight evaluation measures only for adolescent females. Programs that address negative aspects of social comparison may be important in preventing both obesity and eating disorder symptoms in adolescent females. PMID:26403505

Discriminant WSRC for Large-Scale Plant Species Recognition.

PubMed

Zhang, Shanwen; Zhang, Chuanlei; Zhu, Yihai; You, Zhuhong

2017-01-01

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Metabolically healthy obesity from childhood to adulthood - Does weight status alone matter?

PubMed

Blüher, Susann; Schwarz, Peter

2014-09-01

Up to 30% of obese people do not display the "typical" metabolic obesity-associated complications. For this group of patients, the term "metabolically healthy obese (MHO)" has been established during the past years and has been the focus of research activities. The development and severity of insulin resistance as well as (subclinical) inflammations seems to play a key role in distinguishing metabolically healthy from metabolically non-healthy individuals. However, an internationally consistent and accepted classification that might also include inflammatory markers as well as features of non-alcoholic fatty liver disease is missing to date, and available data - in terms of prevalence, definition and severity - are heterogeneous, both during childhood/adolescence and during adulthood. In addition, the impact of MHO on future morbidity and mortality compared to obese, metabolically non-healthy as well as normal weight, metabolically healthy individuals is absolutely not clear to date and even conflicting. This review summarizes salient literature related to that topic and provides insight into our current understanding of MHO, covering all age spans from childhood to adulthood. Copyright © 2014 Elsevier Inc. All rights reserved.

A consensual neural network

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

1991-01-01

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

Tests of Several Model Nacelle-Propeller Arrangements in Front of a Wing

NASA Technical Reports Server (NTRS)

McHugh, James G.

1939-01-01

An investigation was conducted in the N.A.C.A. 20-foot wind tunnel to determine the drag, the propulsive and net efficiencies, and the cooling characteristics of severa1 scale-model arrangements of air-cooled radial-engine nacelles and present-day propellers in front of an 18- percent-thick, 5- by 15-foot airfoil. This report deals with an investigation of wing-nacelle arrangements simulating the geometric proportions of airplanes in the 40,000- to 70,000- pound weight classification and having the nacelles located in the vicinity of the optimum location determined from the earlier tests.

Verification, refinement, and applicability of long-term pavement performance vehicle classification rules.

DOT National Transportation Integrated Search

2014-11-01

The Long-Term Pavement Performance (LTPP) project has developed and deployed a set of rules for converting axle spacing and weight data into estimates of a vehicles classification. These rules are being used at Transportation Pooled Fund Study (TP...

A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2016-10-01

This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

Correction Equations to Adjust Self-Reported Height and Weight for Obesity Estimates among College Students

ERIC Educational Resources Information Center

Mozumdar, Arupendra; Liguori, Gary

2011-01-01

The purposes of this study were to generate correction equations for self-reported height and weight quartiles and to test the accuracy of the body mass index (BMI) classification based on corrected self-reported height and weight among 739 male and 434 female college students. The BMIqc (from height and weight quartile-specific, corrected…

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Bin

2004-03-01

The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence.

PubMed

Goshvarpour, Ateke; Goshvarpour, Atefeh

2018-04-30

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.

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

Fetal Cardiac Responding: A Correlate of Birth Weight and Neonatal Behavior.

ERIC Educational Resources Information Center

Emory, Eugene K.; Noonan, John R.

1984-01-01

Explores whether an empirical classification of healthy fetuses as fetal heart rate accelerators or decelerators would predict birth weight and neonatal behavior scored with the Brazelton Neonatal Behavior Assessment Scale. (Author/RH)

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

European consensus meeting of ARM-Net members concerning diagnosis and early management of newborns with anorectal malformations.

PubMed

van der Steeg, H J J; Schmiedeke, E; Bagolan, P; Broens, P; Demirogullari, B; Garcia-Vazquez, A; Grasshoff-Derr, S; Lacher, M; Leva, E; Makedonsky, I; Sloots, C E J; Schwarzer, N; Aminoff, D; Schipper, M; Jenetzky, E; van Rooij, I A L M; Giuliani, S; Crétolle, C; Holland Cunz, S; Midrio, P; de Blaauw, I

2015-03-01

The ARM-Net (anorectal malformation network) consortium held a consensus meeting in which the classification of ARM and preoperative workup were evaluated with the aim of improving monitoring of treatment and outcome. The Krickenbeck classification of ARM and preoperative workup suggested by Levitt and Peña, used as a template, were discussed, and a collaborative consensus was achieved. The Krickenbeck classification is appropriate in describing ARM for clinical use. The preoperative workup was slightly modified. In males with a visible fistula, no cross-table lateral X-ray is needed and an anoplasty or (mini-) posterior sagittal anorectoplasty can directly be performed. In females with a small vestibular fistula (Hegar size <5 mm), a primary repair or colostomy is recommended; the repair may be delayed if the fistula admits a Hegar size >5 mm, and in the meantime, gentle painless dilatations can be performed. In both male and female perineal fistula and either a low birth weight (<2,000 g) or severe associated congenital anomalies, prolonged preoperative painless dilatations might be indicated to decrease perioperative morbidity caused by general anesthesia. The Krickenbeck classification is appropriate in describing ARM for clinical use. Some minor modifications to the preoperative workup by Levitt and Peña have been introduced in order to refine terminology and establish a comprehensive preoperative workup.

Creating a Canonical Scientific and Technical Information Classification System for NCSTRL+

NASA Technical Reports Server (NTRS)

Tiffany, Melissa E.; Nelson, Michael L.

1998-01-01

The purpose of this paper is to describe the new subject classification system for the NCSTRL+ project. NCSTRL+ is a canonical digital library (DL) based on the Networked Computer Science Technical Report Library (NCSTRL). The current NCSTRL+ classification system uses the NASA Scientific and Technical (STI) subject classifications, which has a bias towards the aerospace, aeronautics, and engineering disciplines. Examination of other scientific and technical information classification systems showed similar discipline-centric weaknesses. Traditional, library-oriented classification systems represented all disciplines, but were too generalized to serve the needs of a scientific and technically oriented digital library. Lack of a suitable existing classification system led to the creation of a lightweight, balanced, general classification system that allows the mapping of more specialized classification schemes into the new framework. We have developed the following classification system to give equal weight to all STI disciplines, while being compact and lightweight.

Analysis of vehicle classification and truck weight data of the New England states

DOT National Transportation Integrated Search

1998-09-01

This report is about a statistical analysis of 1995-96 classification and weigh in motion (WIM) data from 17 continuous traffic-monitoring sites in New England. It documents work performed by Oak Ridge National Laboratory in fulfillment of 'Analysis ...

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.

Dairy cow disability weights.

PubMed

McConnel, Craig S; McNeil, Ashleigh A; Hadrich, Joleen C; Lombard, Jason E; Garry, Franklyn B; Heller, Jane

2017-08-01

Over the past 175 years, data related to human disease and death have progressed to a summary measure of population health, the Disability-Adjusted Life Year (DALY). As dairies have intensified there has been no equivalent measure of the impact of disease on the productive life and well-being of animals. The development of a disease-adjusted metric requires a consistent set of disability weights that reflect the relative severity of important diseases. The objective of this study was to use an international survey of dairy authorities to derive disability weights for primary disease categories recorded on dairies. National and international dairy health and management authorities were contacted through professional organizations, dairy industry publications and conferences, and industry contacts. Estimates of minimum, most likely, and maximum disability weights were derived for 12 common dairy cow diseases. Survey participants were asked to estimate the impact of each disease on overall health and milk production. Diseases were classified from 1 (minimal adverse effects) to 10 (death). The data was modelled using BetaPERT distributions to demonstrate the variation in these dynamic disease processes, and to identify the most likely aggregated disability weights for each disease classification. A single disability weight was assigned to each disease using the average of the combined medians for the minimum, most likely, and maximum severity scores. A total of 96 respondents provided estimates of disability weights. The final disability weight values resulted in the following order from least to most severe: retained placenta, diarrhea, ketosis, metritis, mastitis, milk fever, lame (hoof only), calving trauma, left displaced abomasum, pneumonia, musculoskeletal injury (leg, hip, back), and right displaced abomasum. The peaks of the probability density functions indicated that for certain disease states such as retained placenta there was a relatively narrow range of expected impact whereas other diseases elicited a wider breadth of impact. This was particularly apparent with respect to calving trauma, lameness and musculoskeletal injury, all of which could be redefined using gradients of severity or accounting for sequelae. These disability weight distributions serve as an initial step in the development of the disease-adjusted lactation (DALact) metric. They will be used to assess the time lost due to dynamic phases of dairy cow diseases and injuries. Prioritizing health interventions based on time expands the discussion of animal health to view profits and losses in light of the quality and length of life. Copyright © 2017 Elsevier B.V. All rights reserved.

Bimanual Capacity of Children With Cerebral Palsy: Intra- and Interrater Reliability of a Revised Edition of the Bimanual Fine Motor Function Classification.

PubMed

Elvrum, Ann-Kristin G; Beckung, Eva; Sæther, Rannei; Lydersen, Stian; Vik, Torstein; Himmelmann, Kate

2017-08-01

To develop a revised edition of the Bimanual Fine Motor Function (BFMF 2), as a classification of fine motor capacity in children with cerebral palsy (CP), and establish intra- and interrater reliability of this edition. The content of the original BFMF was discussed by an expert panel, resulting in a revised edition comprising the original description of the classification levels, but in addition including figures with specific explanatory text. Four professionals classified fine motor function of 79 children (3-17 years; 45 boys) who represented all subtypes of CP and Manual Ability Classification levels (I-V). Intra- and inter-rater reliability was assessed using overall intra-class correlation coefficient (ICC), and Cohen's quadratic weighted kappa. The overall ICC was 0.86. Cohen's weighted kappa indicated high intra-rater (к w : >0.90) and inter-rater (к w : >0.85) reliability. The revised BFMF 2 had high intra- and interrater reliability. The classification levels could be determined from short video recordings (<5 minutes), using the figures and precise descriptions of the fine motor function levels included in the BFMF 2. Thus, the BFMF 2 may be a feasible and useful classification of fine motor capacity both in research and in clinical practice.

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

PubMed

Greene, Nathaniel H; Kernic, Mary A; Vavilala, Monica S; Rivara, Frederick P

2015-10-01

Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets. 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.

Cachexia: clinical features when inflammation drives malnutrition.

PubMed

Laviano, Alessandro; Koverech, Angela; Mari, Alessia

2015-11-01

Cachexia is a clinically relevant syndrome which impacts on quality of life, morbidity and mortality of patients suffering from acute and chronic diseases. The hallmark of cachexia is muscle loss, which is triggered by disease-associated inflammatory response. Cachexia is a continuum and therefore a staging system is needed. Initially, a three-stage system (i.e. pre-cachexia, cachexia and refractory cachexia) was proposed. More recent evidence supports the use of a five-stage classification system, based on patient's BMI and severity of weight loss, to better predict clinical outcome. Also, large clinical trials in cancer patients demonstrated that cachexia emerging during chemotherapy has greater influence on survival than weight loss at baseline. Therefore, becoming widely accepted is the importance of routinely monitoring patients' nutritional status to detect early changes and diagnose cachexia in its early phases. Although cachexia is associated with the presence of anabolic resistance, it has been shown that sustained yet physiological hyperaminoacidaemia, as well as the use of specific nutrients, is able to overcome impaired protein synthesis and revert catabolism. More importantly, clinical evidence demonstrates that preservation of nutritional status during chemotherapy or improvement of body weight after weight loss is associated with longer survival in cancer patients.

Eating disorders and weight control behaviors change over a collegiate sport season.

PubMed

Thompson, Alexandra; Petrie, Trent; Anderson, Carlin

2017-09-01

Determine whether the prevalence of eating disorder classifications (i.e., clinical eating disorder, subclinical eating disorder, and asymptomatic) and pathogenic weight control behaviors (e.g., bingeing, vomiting) change over a five-month sport season. Longitudinal study. Female collegiate gymnasts and swimmers (N=325) completed the Questionnaire for Eating Disorder Diagnoses as well as six items from the Bulimia Test-Revised at Time 1 (two weeks into the beginning of their athletic season) and Time 2 (final two weeks of the athletic season); data collections were separated by five months. Over the course of the season, 90% of the athletes (18 out of 20) retained a clinical eating disorder diagnosis or moved to the subclinical classification. Of the 83 subclinical athletes at Time 1, 37.3% persisted with that classification and 10.8% developed a clinical eating disorder; the remainder became asymptomatic/healthy eaters by Time 2. The majority of Time 1 asymptomatic athletes (92.3%) remained so at Time 2. Exercise and dieting/fasting were the most frequent forms of weight control behaviors, though each was used less frequently at Time 2 (exercise=35.4%; dieting=9.2%) than at Time 1 (exercise=42.5%; dieting=12.3%). Eating disorder classifications, particularly clinical and subclinical, remain stable across a competitive season, supporting the need for early detection and purposeful intervention. Athletes engage in weight control behaviors that may be reinforced in the sport environment (e.g., supplemental exercise), making identification more challenging for sports medicine professionals. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Cognitive and motor function of neurologically impaired extremely low birth weight children.

PubMed

Bernardo, Janine; Friedman, Harriet; Minich, Nori; Taylor, H Gerry; Wilson-Costello, Deanne; Hack, Maureen

2015-01-01

Rates of neurological impairment among extremely low birth weight children (ELBW [<1 kg]) have decreased since 2000; however, their functioning is unexamined. To compare motor and cognitive functioning of ELBW children with neurological impairment, including cerebral palsy and severe hypotonia/hypertonia, between two periods: 1990 to 1999 (n=83) and 2000 to 2005 (n=34). Measures of function at 20 months corrected age included the Mental and Psychomotor Developmental Indexes of the Bayley Scales of Infant Development and the Gross Motor Functional Classification System as primary outcomes and individual motor function items as secondary outcomes. Analysis failed to reveal significant differences for the primary outcomes, although during 2000 to 2005, sitting significantly improved in children with neurological impairment (P=0.003). Decreases in rates of neurological impairment among ELBW children have been accompanied by a suggestion of improved motor function, although cognitive function has not changed.

A semi-automated method for bone age assessment using cervical vertebral maturation.

PubMed

Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T

2012-07-01

To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.

Association between recovery from Bell's palsy and body mass index.

PubMed

Choi, S A; Shim, H S; Jung, J Y; Kim, H J; Kim, S H; Byun, J Y; Park, M S; Yeo, S G

2017-06-01

Although many factors have been found to be involved in recovery from Bell's palsy, no study has investigated the association between recovery from Bell's palsy and obesity. This study therefore evaluated the association between recovery from Bell's palsy and body mass index (BMI). Subjects were classified into five groups based on BMI (kg/m 2 ). Demographic and clinical characteristics were compared among these groups. Assessed factors included sex, age, time from paralysis to visiting a hospital, the presence of comorbidities such as diabetes mellitus and hypertension, degree of initial facial nerve paralysis by House-Brackmann (H-B) grade and neurophysiological testing, and final recovery rate. Based on BMI, 37 subjects were classified as underweight, 169 as normal weight, 140 as overweight, 155 as obese and 42 as severely obese. Classification of the degree of initial facial nerve paralysis as moderate or severe, according to H-B grade and electroneurography, showed no difference in severity of initial facial paralysis among the five groups (P > 0.05). However, the final recovery rate was significantly higher in the normal weight than in the underweight or obese group (P < 0.05). Obesity or underweight had no effect on the severity of initial facial paralysis, but the final recovery rate was lower in the obese and underweight groups than in the normal group. © 2016 John Wiley & Sons Ltd.

Hyaluronic acid and chondroitin sulfate content of osteoarthritic human knee cartilage: site-specific correlation with weight-bearing force based on femorotibial angle measurement.

PubMed

Otsuki, Shuhei; Nakajima, Mikio; Lotz, Martin; Kinoshita, Mitsuo

2008-09-01

This study analyzed glycosaminoglycan (GAG) content in specific compartments of the knee joint to determine the impact of malalignment and helped refine indications for osteotomy. To assess malalignment, the radiological femorotibial angle (FTA) was measured and knee joints were also graded for OA severity with the Kellgren/Lawrence (K/L) classification. Cartilage samples were obtained from 36 knees of 32 OA patients undergoing total knee replacement surgery. Explants were harvested from the medial femoral condyle (MFC), lateral femoral condyle (LFC), patellar groove (PG), and lateral posterior femoral condyle (LPC). Concentrations of hyaluronic acid (HA) and chondroitin sulfate (CS) were measured by high-performance liquid chromatography (HPLC). With OA severity, the average FTA significantly increased. HA and CS content in MFC was negatively correlated with radiographic FTA. In LFC, HA ratio, which is HA content in lateral condyle divided by medial condyle and chondroitin 6 sulfate, increased until about 190 degrees FTA. Importantly, at >190 degrees these contents were significantly decreased. HA and CS content of the femoral condyle shows topographic differences that are related to OA grade and weight-bearing force based on FTA. The clinical relevance is that osteotomy may not be indicated for patients with severe varus (>190 degrees) abnormalities. (c) 2008 Orthopaedic Research Society

Determination of quality television programmes based on sentiment analysis on Twitter

NASA Astrophysics Data System (ADS)

Amalia, A.; Oktinas, W.; Aulia, I.; Rahmat, R. F.

2018-03-01

Public sentiment from social media like Twitter can be used as one of the indicators to determine the quality of TV Programmes. In this study, we implemented information extraction on Twitter by using sentiment analysis method to assess the quality of TV Programmes. The first stage of this study is pre-processing which consists of cleansing, case folding, tokenizing, stop-word removal, stemming, and redundancy filtering. The next stage is weighting process for every single word by using TF-IDF method. The last step of this study is the sentiment classification process which is divided into three sentiment category which is positive, negative and neutral. We classify the TV programmes into several categories such as news, children, or films/soap operas. We implemented an improved k-nearest neighbor method in classification 4000 twitter status, for four biggest TV stations in Indonesia, with ratio 70% data for training and 30% of data for the testing process. The result obtained from this research generated the highest accuracy with k=10 as big as 90%.

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

Wiltse, J.

Issues presented are related to classification of weight of evidence in cancer risk assessments. The focus in this paper is on lines of evidence used in constructing a conclusion about potential human carcinogenicity. The paper also discusses issues that are mistakenly addressed as classification issues but are really part of the risk assessment process. 2 figs.

Analysis of vehicle classification and truck weight data of the New England States : is data sharing a good idea?

DOT National Transportation Integrated Search

1998-01-01

This paper is about a statistical research analysis of 1995-96 classification and weigh in motion : (WIM) data from seventeen continuous traffic-monitoring sites in New England. Data screening is : discussed briefly, and a cusum data quality control ...

7 CFR 51.1404 - Tolerances.

Code of Federal Regulations, 2012 CFR

2012-01-01

... the grade other than for skin color. (3) For loose extraneous or foreign material, by weight. (i) 0.5... requirements for the grade or any specified color classification, including therein not more than 7 percent for... meet the color requirements for the grade or for any specified color classification, but which are not...

42 CFR 412.620 - Patient classification system.

Code of Federal Regulations, 2013 CFR

2013-10-01

... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2013-10-01 2013-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...

42 CFR 412.620 - Patient classification system.

Code of Federal Regulations, 2012 CFR

2012-10-01

... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2012-10-01 2012-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...

42 CFR 412.620 - Patient classification system.

Code of Federal Regulations, 2014 CFR

2014-10-01

... weighting factors to reflect changes in— (1) Treatment patterns; (2) Technology; (3) Number of discharges... 42 Public Health 2 2014-10-01 2014-10-01 false Patient classification system. 412.620 Section 412... rehabilitation facilities into mutually exclusive case-mix groups. (2) For purposes of this subpart, case-mix...

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

Classification of different degrees of adiposity in sedentary rats.

PubMed

Leopoldo, A S; Lima-Leopoldo, A P; Nascimento, A F; Luvizotto, R A M; Sugizaki, M M; Campos, D H S; da Silva, D C T; Padovani, C R; Cicogna, A C

2016-01-01

In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups.

Standardizing Foot-Type Classification Using Arch Index Values

PubMed Central

Weil, Rich; de Boer, Emily

2012-01-01

ABSTRACT Purpose: The lack of a reliable classification standard for foot type makes drawing conclusions from existing research and clinical decisions difficult, since different foot types may move and respond to treatment differently. The purpose of this study was to determine interrater agreement for foot-type classification based on photo-box-derived arch index values. Method: For this correlational study with two raters, a sample of 11 healthy volunteers with normal to obese body mass indices was recruited from both a community weight-loss programme and a programme in physical therapy. Arch index was calculated using AutoCAD software from footprint photographs obtained via mirrored photo-box. Classification as high-arched, normal, or low-arched foot type was based on arch index values. Reliability of the arch index was determined with intra-class correlations; agreement on foot-type classification was determined using quadratic weighted kappa (κw). Results: Average arch index was 0.215 for one tester and 0.219 for the second tester, with an overall range of 0.017 to 0.370. Both testers classified 6 feet as low-arched, 9 feet as normal, and 7 feet as high-arched. Interrater reliability for the arch index was ICC=0.90; interrater agreement for foot-type classification was κw=0.923. Conclusions: Classification of foot type based on arch index values derived from plantar footprint photographs obtained via mirrored photo-box showed excellent reliability in people with varying BMI. Foot-type classification may help clinicians and researchers subdivide sample populations to better differentiate mobility, gait, or treatment effects among foot types. PMID:23729964

Pancreatic abnormalities detected by endoscopic ultrasound (EUS) in patients without clinical signs of pancreatic disease: any difference between standard and Rosemont classification scoring?

PubMed

Petrone, Maria Chiara; Terracciano, Fulvia; Perri, Francesco; Carrara, Silvia; Cavestro, Giulia Martina; Mariani, Alberto; Testoni, Pier Alberto; Arcidiacono, Paolo Giorgio

2014-01-01

The prevalence of nine EUS features of chronic pancreatitis (CP) according to the standard Wiersema classification has been investigated in 489 patients undergoing EUS for an indication not related to pancreatico-biliary disease. We showed that 82 subjects (16.8%) had at least one ductular or parenchymal abnormality. Among them, 18 (3.7% of study population) had ≥3 Wiersema criteria suggestive of CP. Recently, a new classification (Rosemont) of EUS findings consistent, suggestive or indeterminate for CP has been proposed. To stratify healthy subjects into different subgroups on the basis of EUS features of CP according to the Wiersema and Rosemont classifications and to evaluate the agreement in the diagnosis of CP with the two scoring systems. Weighted kappa statistics was computed to evaluate the strength of agreement between the two scoring systems. Univariate and multivariate analysis between any EUS abnormality and habits were performed. Eighty-two EUS videos were reviewed. Using the Wiersema classification, 18 subjects showed ≥3 EUS features suggestive of CP. The EUS diagnosis of CP in these 18 subjects was considered as consistent in only one patient, according to Rosemont classification. Weighted Kappa statistics was 0.34 showing that the strength of agreement was 'fair'. Alcohol use and smoking were identified as risk factors for having pancreatic abnormalities on EUS. The prevalence of EUS features consistent or suggestive of CP in healthy subjects according to the Rosemont classification is lower than that assessed by Wiersema criteria. In that regard the Rosemont classification seems to be more accurate in excluding clinically relevant CP. Overall agreement between the two classifications is fair. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Differential risk of injury in child occupants by passenger car classification.

PubMed

Kallan, Michael J; Durbin, Dennis R; Elliott, Michael R; Menon, Rajiv A; Winston, Flaura K

2003-01-01

In the United States, passenger cars are the most common passenger vehicle, yet they vary widely in size and crashworthiness. Using data collected from a population-based sample of crashes in State Farm-insured vehicles, we quantified the risk of injury to child occupants by passenger car size and classification. Injury risk is predicted by vehicle weight; however, there is an increased risk in both Large vs. Luxury and Sports vs. Small cars, despite similar average vehicle weights in both comparisons. Parents who are purchasing passenger cars should strongly consider the size of the vehicle and its crashworthiness.

Differential Risk of Injury in Child Occupants by Passenger Car Classification

PubMed Central

Kallan, Michael J.; Durbin, Dennis R.; Elliott, Michael R.; Menon, Rajiv A.; Winston, Flaura K.

2003-01-01

In the United States, passenger cars are the most common passenger vehicle, yet they vary widely in size and crashworthiness. Using data collected from a population-based sample of crashes in State Farm-insured vehicles, we quantified the risk of injury to child occupants by passenger car size and classification. Injury risk is predicted by vehicle weight; however, there is an increased risk in both Large vs. Luxury and Sports vs. Small cars, despite similar average vehicle weights in both comparisons. Parents who are purchasing passenger cars should strongly consider the size of the vehicle and its crashworthiness. PMID:12941234

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

Longitudinal Study of Oropharyngeal Dysphagia in Preschool Children With Cerebral Palsy.

PubMed

Benfer, Katherine A; Weir, Kelly A; Bell, Kristie L; Ware, Robert S; Davies, Peter S; Boyd, Roslyn N

2016-04-01

To determine changes in prevalence and severity of oropharyngeal dysphagia (OPD) in children with cerebral palsy (CP) and the relationship to health outcomes. Longitudinal cohort study. Community and tertiary institutions. Children (N=53, 33 boys) with a confirmed diagnosis of CP assessed first at 18 to 24 months (Assessment 1: mean age ± SD, 22.9±2.9 mo corrected age; Gross Motor Function Classification System [GMFCS]: I, n=22; II, n=7; III, n=11; IV, n=5; V, n=8) and at 36 months (Assessment 2). Not applicable. OPD was classified using the Dysphagia Disorders Survey (DDS) and signs suggestive of pharyngeal dysphagia. Nutritional status was measured using Z scores for weight, height, and body mass index (BMI). Gross motor skills were classified on GMFCS and motor type/distribution. Prevalence of OPD decreased from 62% to 59% between the ages of 18 to 24 months and 36 months. Thirty percent of children had an improvement in severity of OPD (greater than smallest detectable change), and 4% had worse OPD. Gross motor function was strongly associated with OPD at both assessments, on the DDS (Assessment 1: odds ratio [OR]=20.3, P=.011; Assessment 2: OR=28.9, P=.002), pharyngeal signs (Assessment 1: OR=10.6, P=.007; Assessment 2: OR=15.8, P=.003), and OPD severity (Assessment 1: β=6.1, P<.001; Assessment 2: β=5.5, P<.001). OPD at 18 to 24 months was related to health outcomes at 36 months: low Z scores for weight (adjusted β=1.2, P=.03) and BMI (adjusted β=1.1, P=.048), and increased parent stress (adjusted OR=1.1, P=.049). Classification and severity of OPD remained relatively stable between 18 to 24 months and 36 months. Gross motor function was the best predictor of OPD. These findings contribute to developing more effective screening processes that consider critical developmental transitions that are anticipated to present challenges for children from each of the GMFCS levels. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Load Weight Classification of The Quayside Container Crane Based On K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Bingqian; Hu, Xiong; Tang, Gang; Wang, Yide

2017-07-01

The precise knowledge of the load weight of each operation of the quayside container crane is important for accurately assessing the service life of the crane. The load weight is directly related to the vibration intensity. Through the study on the vibration of the hoist motor of the crane in radial and axial directions, we can classify the load using K-means clustering algorithm and quantitative statistical analysis. Vibration in radial direction is significantly and positively correlated with that in axial direction by correlation analysis, which means that we can use the data only in one of the directions to carry out the study improving then the efficiency without degrading the accuracy of load classification. The proposed method can well represent the real-time working condition of the crane.

FPGA Implementation of Generalized Hebbian Algorithm for Texture Classification

PubMed Central

Lin, Shiow-Jyu; Hwang, Wen-Jyi; Lee, Wei-Hao

2012-01-01

This paper presents a novel hardware architecture for principal component analysis. The architecture is based on the Generalized Hebbian Algorithm (GHA) because of its simplicity and effectiveness. The architecture is separated into three portions: the weight vector updating unit, the principal computation unit and the memory unit. In the weight vector updating unit, the computation of different synaptic weight vectors shares the same circuit for reducing the area costs. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is physically implemented by Field Programmable Gate Array (FPGA). It is embedded in a System-On-Programmable-Chip (SOPC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs. PMID:22778640

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

NASA Astrophysics Data System (ADS)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

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.

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Severity of Airflow Obstruction in Chronic Obstructive Pulmonary Disease (COPD): Proposal for a New Classification.

PubMed

Coton, Sonia; Vollmer, William M; Bateman, Eric; Marks, Guy B; Tan, Wan; Mejza, Filip; Juvekar, Sanjay; Janson, Christer; Mortimer, Kevin; P A, Mahesh; Buist, A Sonia; Burney, Peter G J

2017-10-01

Current classifications of Chronic Obstructive Pulmonary Disease (COPD) severity are complex and do not grade levels of obstruction. Obstruction is a simpler construct and independent of ethnicity. We constructed an index of obstruction severity based on the FEV 1 /FVC ratio, with cut-points dividing the Burden of Obstructive Lung Disease (BOLD) study population into four similarly sized strata to those created by the GOLD criteria that uses FEV 1 . We measured the agreement between classifications and the validity of the FEV 1 -based classification in identifying the level of obstruction as defined by the new groupings. We compared the strengths of association of each classification with quality of life (QoL), MRC dyspnoea score and the self-reported exacerbation rate. Agreement between classifications was only fair. FEV 1 -based criteria for moderate COPD identified only 79% of those with moderate obstruction and misclassified half of the participants with mild obstruction as having more severe COPD. Both scales were equally strongly associated with QoL, exertional dyspnoea and respiratory exacerbations. Severity assessed using the FEV 1 /FVC ratio is only in moderate agreement with the severity assessed using FEV 1 but is equally strongly associated with other outcomes. Severity assessed using the FEV 1 /FVC ratio is likely to be independent of ethnicity.

Identification of extremely premature infants at high risk of rehospitalization.

PubMed

Ambalavanan, Namasivayam; Carlo, Waldemar A; McDonald, Scott A; Yao, Qing; Das, Abhik; Higgins, Rosemary D

2011-11-01

Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002-2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%-42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge.

Identification of Extremely Premature Infants at High Risk of Rehospitalization

PubMed Central

Carlo, Waldemar A.; McDonald, Scott A.; Yao, Qing; Das, Abhik; Higgins, Rosemary D.

2011-01-01

OBJECTIVE: Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. METHODS: Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002–2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. RESULTS: A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%–42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. CONCLUSIONS: The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge. PMID:22007016

[Determinant-based classification of acute pancreatitis severity. International multidisciplinary classification of acute pancreatitis severity: the 2013 German edition].

PubMed

Layer, P; Dellinger, E P; Forsmark, C E; Lévy, P; Maraví-Poma, E; Shimosegawa, T; Siriwardena, A K; Uomo, G; Whitcomb, D C; Windsor, J A; Petrov, M S

2013-06-01

The aim of this study was to develop a new international classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of published evidence, and worldwide consultation. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of pancreatologists but suboptimal because these definitions are based on empiric descriptions of occurrences that are merely associated with severity. A personal invitation to contribute to the development of a new international classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensive medicine specialists, and radiologists who are currently active in clinical research on acute pancreatitis. The invitation was not limited to members of certain associations or residents of certain countries. A global Web-based survey was conducted and a dedicated international symposium was organised to bring contributors from different disciplines together and discuss the concept and definitions. The new international classification is based on the actual local and systemic determinants of severity, rather than descriptions of events that are correlated with severity. The local determinant relates to whether there is (peri)pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another such that the presence of both infected (peri)pancreatic necrosis and persistent organ failure have a greater effect on severity than either determinant alone. The derivation of a classification based on the above principles results in 4 categories of severity - mild, moderate, severe, and critical. This classification is the result of a consultative process amongst pancreatologists from 49 countries spanning North America, South America, Europe, Asia, Oceania, and Africa. It provides a set of concise up-to-date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research. This ensures that the determinant-based classification can be used in a uniform manner throughout the world. © Georg Thieme Verlag KG Stuttgart · New York.

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site : final report.

DOT National Transportation Integrated Search

1991-07-01

Oregon has twelve pavement test sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot proj...

Piezo-electric automatic vehicle classification system : Oregon Department of Transportation with Castle Rock Consultants for a SHRP Long Term Pavement Performance Site.

DOT National Transportation Integrated Search

1990-05-01

Oregon has twelve sites that are part of the Strategic Highway Research Program (SHRP), Long Term Pavement Performance (LTPP) studies. Part of the data gathering on these sites involves vehicle weight and classification. This pilot project was to hel...

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.

Use of Magnetic Resonance Imaging as Well as Clinical Disease Activity in the Clinical Classification of Multiple Sclerosis and Assessment of Its Course

PubMed Central

Dhib-Jalbut, Suhayl; Dowling, Peter; Durelli, Luca; Ford, Corey; Giovannoni, Gavin; Halper, June; Harris, Colleen; Herbert, Joseph; Li, David; Lincoln, John A.; Lisak, Robert; Lublin, Fred D.; Lucchinetti, Claudia F.; Moore, Wayne; Naismith, Robert T.; Oehninger, Carlos; Simon, Jack; Sormani, Maria Pia

2012-01-01

It has recently been suggested that the Lublin-Reingold clinical classification of multiple sclerosis (MS) be modified to include the use of magnetic resonance imaging (MRI). An international consensus conference sponsored by the Consortium of Multiple Sclerosis Centers (CMSC) was held from March 5 to 7, 2010, to review the available evidence on the need for such modification of the Lublin-Reingold criteria and whether the addition of MRI or other biomarkers might lead to a better understanding of MS pathophysiology and disease course over time. The conference participants concluded that evidence of new MRI gadolinium-enhancing (Gd+) T1-weighted lesions and unequivocally new or enlarging T2-weighted lesions (subclinical activity, subclinical relapses) should be added to the clinical classification of MS in distinguishing relapsing inflammatory from progressive forms of the disease. The consensus was that these changes to the classification system would provide more rigorous definitions and categorization of MS course, leading to better insights as to the evolution and treatment of MS. PMID:24453741

Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

PubMed

McRoy, Susan; Jones, Sean; Kurmally, Adam

2016-09-01

This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions. © The Author(s) 2015.

An approach for automatic classification of grouper vocalizations with passive acoustic monitoring.

PubMed

Ibrahim, Ali K; Chérubin, Laurent M; Zhuang, Hanqi; Schärer Umpierre, Michelle T; Dalgleish, Fraser; Erdol, Nurgun; Ouyang, B; Dalgleish, A

2018-02-01

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

Performance of resonant radar target identification algorithms using intra-class weighting functions

NASA Astrophysics Data System (ADS)

Mustafa, A.

The use of calibrated resonant-region radar cross section (RCS) measurements of targets for the classification of large aircraft is discussed. Errors in the RCS estimate of full scale aircraft flying over an ocean, introduced by the ionospheric variability and the sea conditions were studied. The Weighted Target Representative (WTR) classification algorithm was developed, implemented, tested and compared with the nearest neighbor (NN) algorithm. The WTR-algorithm has a low sensitivity to the uncertainty in the aspect angle of the unknown target returns. In addition, this algorithm was based on the development of a new catalog of representative data which reduces the storage requirements and increases the computational efficiency of the classification system compared to the NN-algorithm. Experiments were designed to study and evaluate the characteristics of the WTR- and the NN-algorithms, investigate the classifiability of targets and study the relative behavior of the number of misclassifications as a function of the target backscatter features. The classification results and statistics were shown in the form of performance curves, performance tables and confusion tables.

Estimating the concordance probability in a survival analysis with a discrete number of risk groups.

PubMed

Heller, Glenn; Mo, Qianxing

2016-04-01

A clinical risk classification system is an important component of a treatment decision algorithm. A measure used to assess the strength of a risk classification system is discrimination, and when the outcome is survival time, the most commonly applied global measure of discrimination is the concordance probability. The concordance probability represents the pairwise probability of lower patient risk given longer survival time. The c-index and the concordance probability estimate have been used to estimate the concordance probability when patient-specific risk scores are continuous. In the current paper, the concordance probability estimate and an inverse probability censoring weighted c-index are modified to account for discrete risk scores. Simulations are generated to assess the finite sample properties of the concordance probability estimate and the weighted c-index. An application of these measures of discriminatory power to a metastatic prostate cancer risk classification system is examined.

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

Data-driven automated acoustic analysis of human infant vocalizations using neural network tools.

PubMed

Warlaumont, Anne S; Oller, D Kimbrough; Buder, Eugene H; Dale, Rick; Kozma, Robert

2010-04-01

Acoustic analysis of infant vocalizations has typically employed traditional acoustic measures drawn from adult speech acoustics, such as f(0), duration, formant frequencies, amplitude, and pitch perturbation. Here an alternative and complementary method is proposed in which data-derived spectrographic features are central. 1-s-long spectrograms of vocalizations produced by six infants recorded longitudinally between ages 3 and 11 months are analyzed using a neural network consisting of a self-organizing map and a single-layer perceptron. The self-organizing map acquires a set of holistic, data-derived spectrographic receptive fields. The single-layer perceptron receives self-organizing map activations as input and is trained to classify utterances into prelinguistic phonatory categories (squeal, vocant, or growl), identify the ages at which they were produced, and identify the individuals who produced them. Classification performance was significantly better than chance for all three classification tasks. Performance is compared to another popular architecture, the fully supervised multilayer perceptron. In addition, the network's weights and patterns of activation are explored from several angles, for example, through traditional acoustic measurements of the network's receptive fields. Results support the use of this and related tools for deriving holistic acoustic features directly from infant vocalization data and for the automatic classification of infant vocalizations.

Invited commentary: the incremental value of customization in defining abnormal fetal growth status.

PubMed

Zhang, Jun; Sun, Kun

2013-10-15

Reference tools based on birth weight percentiles at a given gestational week have long been used to define fetuses or infants that are small or large for their gestational ages. However, important deficiencies of the birth weight reference are being increasingly recognized. Overwhelming evidence indicates that an ultrasonography-based fetal weight reference should be used to classify fetal and newborn sizes during pregnancy and at birth, respectively. Questions have been raised as to whether further adjustments for race/ethnicity, parity, sex, and maternal height and weight are helpful to improve the accuracy of the classification. In this issue of the Journal, Carberry et al. (Am J Epidemiol. 2013;178(8):1301-1308) show that adjustment for race/ethnicity is useful, but that additional fine tuning for other factors (i.e., full customization) in the classification may not further improve the ability to predict infant morbidity, mortality, and other fetal growth indicators. Thus, the theoretical advantage of full customization may have limited incremental value for pediatric outcomes, particularly in term births. Literature on the prediction of short-term maternal outcomes and very long-term outcomes (adult diseases) is too scarce to draw any conclusions. Given that each additional variable being incorporated in the classification scheme increases complexity and costs in practice, the clinical utility of full customization in obstetric practice requires further testing.

Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

PubMed Central

Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

2016-01-01

Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

The impact of weight classification on safety: timing steps to adapt to external constraints

PubMed Central

Gill, S.V.

2015-01-01

Objectives: The purpose of the current study was to evaluate how weight classification influences safety by examining adults’ ability to meet a timing constraint: walking to the pace of an audio metronome. Methods: With a cross-sectional design, walking parameters were collected as 55 adults with normal (n=30) and overweight (n=25) body mass index scores walked to slow, normal, and fast audio metronome paces. Results: Between group comparisons showed that at the fast pace, those with overweight body mass index (BMI) had longer double limb support and stance times and slower cadences than the normal weight group (all ps<0.05). Examinations of participants’ ability to meet the metronome paces revealed that participants who were overweight had higher cadences at the slow and fast paces (all ps<0.05). Conclusions: Findings suggest that those with overweight BMI alter their gait to maintain biomechanical stability. Understanding how excess weight influences gait adaptation can inform interventions to improve safety for individuals with obesity. PMID:25730658

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Determinant-based classification of acute pancreatitis severity: an international multidisciplinary consultation.

PubMed

Dellinger, E Patchen; Forsmark, Christopher E; Layer, Peter; Lévy, Philippe; Maraví-Poma, Enrique; Petrov, Maxim S; Shimosegawa, Tooru; Siriwardena, Ajith K; Uomo, Generoso; Whitcomb, David C; Windsor, John A

2012-12-01

To develop a new international classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of published evidence, and worldwide consultation. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of pancreatologists but suboptimal because these definitions are based on empiric description of occurrences that are merely associated with severity. A personal invitation to contribute to the development of a new international classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists, and radiologists who are currently active in clinical research on acute pancreatitis. The invitation was not limited to members of certain associations or residents of certain countries. A global Web-based survey was conducted and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new international classification is based on the actual local and systemic determinants of severity, rather than description of events that are correlated with severity. The local determinant relates to whether there is (peri)pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another such that the presence of both infected (peri)pancreatic necrosis and persistent organ failure have a greater effect on severity than either determinant alone. The derivation of a classification based on the above principles results in 4 categories of severity-mild, moderate, severe, and critical. This classification is the result of a consultative process amongst pancreatologists from 49 countries spanning North America, South America, Europe, Asia, Oceania, and Africa. It provides a set of concise up-to-date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research. This ensures that the determinant-based classification can be used in a uniform manner throughout the world.

Feature weight estimation for gene selection: a local hyperlinear learning approach

PubMed Central

2014-01-01

Background Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for selecting a small set of relevant genes, buried in high-dimensional irrelevant noises. RELIEF is a popular and widely used approach for feature selection owing to its low computational cost and high accuracy. However, RELIEF based methods suffer from instability, especially in the presence of noisy and/or high-dimensional outliers. Results We propose an innovative feature weighting algorithm, called LHR, to select informative genes from highly noisy data. LHR is based on RELIEF for feature weighting using classical margin maximization. The key idea of LHR is to estimate the feature weights through local approximation rather than global measurement, which is typically used in existing methods. The weights obtained by our method are very robust in terms of degradation of noisy features, even those with vast dimensions. To demonstrate the performance of our method, extensive experiments involving classification tests have been carried out on both synthetic and real microarray benchmark datasets by combining the proposed technique with standard classifiers, including the support vector machine (SVM), k-nearest neighbor (KNN), hyperplane k-nearest neighbor (HKNN), linear discriminant analysis (LDA) and naive Bayes (NB). Conclusion Experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed feature selection method combined with supervised learning in three aspects: 1) high classification accuracy, 2) excellent robustness to noise and 3) good stability using to various classification algorithms. PMID:24625071

Evaluation of cognitive and social functioning in patients requiring long-term inpatient psychiatric care using the International Classification of Functioning, Disability, and Health: a large-scale, multi-institutional observational study.

PubMed

Kawaguchi, Hideaki; Taguchi, Masamoto; Sukigara, Masune; Sakuragi, Shoji; Sugiyama, Naoya; Chiba, Hisomu; Kawasaki, Tatsuhito

2017-06-15

We comprehensively evaluated cognitive and social functioning in patients requiring long-term inpatient psychiatric care using the International Classification of Functioning, Disability, and Health. We surveyed 1967 patients receiving long-term inpatient psychiatric care. Patients were further categorized into an old long-stay group (n = 892, >5 years in hospitals) and a new long-stay group (n = 1075, 1-5 years in hospitals). We obtained responses for all the International Classification of Functioning, Disability, and Health items in domain b (Body Functions) and domain d (Activities and Participation). We estimated weighted means for each item using the propensity score to adjust for confounding factors. Responses were received from 307 hospitals (response rate of hospitals: 25.5%). Cognitive and social functioning in the old long-stay group was more severely impaired than in the new long-stay group. No statistically significant differences were observed regarding the International Classification of Functioning, Disability, and Health items associated with basic activities of daily living between the two groups. Combined therapy consisting of cognitive remediation and rehabilitation on social functioning for this patient population should be started from the early stage of hospitalization. Non-restrictive, independent environments may also be optimal for this patient population. Implications for rehabilitation Rehabilitation of cognitive and social functioning for patients requiring long-term inpatient psychiatric care should be started in the early stages of hospitalization. In psychiatric fields, the International Classification of Functioning, Disability, and Health checklist could facilitate individualized rehabilitation planning by allowing healthcare professionals to visually assess the comprehensive functioning of each patient using graphics such as radar charts.

Effects of granularity on the natural classification of loose cover layer rock

NASA Astrophysics Data System (ADS)

Zhang, Shuhui; Wang, Peng; Zhang, Zhiqiang

2018-03-01

In the sublevel caving method, with developing depth of underground mines increasing, the ore loss and dilution is become more and more remarkable that is due to the natural classification of loose cover layer rock. Therefore, this paper researches that granularity are one of the main factors affecting the natural classification, and carries out a physical simulation experiment of loose cover layer rock granularity effects of natural classification. Through the experiment we found that granularity has important effect on natural classification. Under the condition of the same weight, we found the closer of granularities that consist of cover layer rock, the less prone to natural classification. Otherwise, it will be prone to natural classification. This study has a guiding significance for a mine, forming a scientific and reasonable cover layer rock, and reducing the ore loss and dilution in the mining process.

A Novel Modulation Classification Approach Using Gabor Filter Network

PubMed Central

Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

2014-01-01

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Weight Measurements and Standards for Soldiers, Phase 2

DTIC Science & Technology

2016-10-01

SUPPLEMENTARY NOTES 14. ABSTRACT The specific aims of the study are to: 1) examine body weight and fat changes associated with participation in a...maintenance of changes in body weight, body fat , and fitness after discontinuation of the promotion associated with the H.E.A.L.T.H. program. The study is a...physical fitness, health, weight, body fat 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 26 19a. NAME OF

Combustion of High Molecular Weight Hydrocarbon Fuels and JP-8 at Moderate Pressures

DTIC Science & Technology

2016-07-26

SECURITY CLASSIFICATION OF: The objective of this research is to characterize combustion of high molecular weight hydrocarbon fuels and jet- fuels (in...Unlimited UU UU UU UU 26-07-2016 1-May-2012 30-Apr-2016 Final Report: Combustion of High Molecular Weight Hydrocarbon Fuels and JP-8 at Moderate...Report: Combustion of High Molecular Weight Hydrocarbon Fuels and JP-8 at Moderate Pressures (Research Area 1: Mechanical Sciences) Report Title The

Recovery of oxygenated ignitable liquids by zeolites, Part I: Novel extraction methodology in fire debris analysis.

PubMed

St Pierre, Kathryne A; Desiderio, Vincent J; Hall, Adam B

2014-07-01

The recovery of low molecular weight oxygenates in fire debris samples is severely compromised by the use of heated passive headspace concentration with an activated charcoal strip, as outlined in ASTM E-1412. The term "oxygenate" is defined herein as a small, polar, organic molecule, such as acetone, methanol, ethanol, or isopropanol, which can be employed as an ignitable liquid and referred to in the ASTM classification scheme as the "oxygenated solvents" class. Although a well accepted technique, the higher affinity of activated carbon strips for heavy molecular weight products over low molecular weight products and hydrocarbons over oxygenated products, it does not allow for efficient recovery of oxygenates such as low molecular weight alcohols and acetone. The objective of this study was to develop and evaluate a novel method for the enhanced recovery of oxygenates from fire debris samples. By optimizing conditions of the heated passive headspace technique, the utilization of zeolites allowed for the successful collection and concentration of oxygenates. The results demonstrated that zeolites increased the recovery of oxygenates by at least 1.5-fold compared to the activated carbon strip and may complement the currently used extraction technique. Copyright © 2014. Published by Elsevier Ireland Ltd.

Bariatric Surgery Promising in Migraine Control: a Controlled Trial on Weight Loss and Its Effect on Migraine Headache.

PubMed

Razeghi Jahromi, Soodeh; Abolhasani, Maryam; Ghorbani, Zeinab; Sadre-Jahani, Solmaz; Alizadeh, Zahra; Talebpour, Mohammad; Meysamie, Alipasha; Togha, Mansoureh

2018-01-01

There is evidence that substantial weight loss through bariatric surgery (BS) may result in short-term improvement of migraine severity. However, it still remains to be seen whether smaller amounts of weight loss have a similar effect on migraine headache. This study has been designed to compare the effects of weight reduction through BS and non-surgical modifications. Migraine characteristics were assessed at 1 month before (T0), 1 month (T1), and 6 months (T2) after BS (vertical sleeve gastrectomy (VSG) (n = 25) or behavioral therapy (BT) (n = 26) in obese women (aged 18-60 years) with migraine headache. Migraine was diagnosed using the International Classification of Headache Disorders (ICHDIIβ) criteria. There was significant reduction in the visual analog scale (VAS) from the baseline to T1 and T2 in both groups. The number of migraine-free days showed a significant increase within each group (p < 0.001). The BS group had a significant reduction in attack duration (p < 0.001) while there were no changes observed within the BT group. Following the adjustment of ANCOVA models for baseline values of migraine characteristics, age, changes in weight, BMI, body fat, and fat-free mass from T0 to T2, the BS group showed statistically significant lower VAS and duration of migraine attacks and a significantly higher number of migraine-free days than the BT group at T1 and T2 (p ≤ 0.028). Our results indicated that far before significant weight reduction after BS (VSG), there was marked alleviation in the severity and duration of migraine and a significant increase in the number of migraine-free days in obese female migraineurs. However, the effects in the BT group were not comparable with the effects in the BS group.

Point spread function based classification of regions for linear digital tomosynthesis

NASA Astrophysics Data System (ADS)

Israni, Kenny; Avinash, Gopal; Li, Baojun

2007-03-01

In digital tomosynthesis, one of the limitations is the presence of out-of-plane blur due to the limited angle acquisition. The point spread function (PSF) characterizes blur in the imaging volume, and is shift-variant in tomosynthesis. The purpose of this research is to classify the tomosynthesis imaging volume into four different categories based on PSF-driven focus criteria. We considered linear tomosynthesis geometry and simple back projection algorithm for reconstruction. The three-dimensional PSF at every pixel in the imaging volume was determined. Intensity profiles were computed for every pixel by integrating the PSF-weighted intensities contained within the line segment defined by the PSF, at each slice. Classification rules based on these intensity profiles were used to categorize image regions. At background and low-frequency pixels, the derived intensity profiles were flat curves with relatively low and high maximum intensities respectively. At in-focus pixels, the maximum intensity of the profiles coincided with the PSF-weighted intensity of the pixel. At out-of-focus pixels, the PSF-weighted intensity of the pixel was always less than the maximum intensity of the profile. We validated our method using human observer classified regions as gold standard. Based on the computed and manual classifications, the mean sensitivity and specificity of the algorithm were 77+/-8.44% and 91+/-4.13% respectively (t=-0.64, p=0.56, DF=4). Such a classification algorithm may assist in mitigating out-of-focus blur from tomosynthesis image slices.

[Multidisciplinar international classification of the severity of acute pancreatitis: Italian version 2013].

PubMed

Uomo, G; Patchen Dellinger, E; Forsmark, C E; Layer, P; Lévy, P; Maravì-Poma, E; Shimosegawa, T; Siriwardena, A K; Whitcomb, D C; Windsor, J A; Petrov, M S

2013-12-01

The aim of this paper was to present the 2013 Italian edition of a new international classification of acute pancreatitis severity. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of pancreatologists but suboptimal because these definitions are based on empiric description of occurrences that are merely associated with severity. A personal invitation to contribute to the development of a new international classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists, and radiologists who are currently active in clinical research on acute pancreatitis. A global web-based survey was conducted and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new international classification is based on the actual local and systemic determinants of severity, rather than description of events that are correlated with severity. The local determinant relates to whether there is (peri)pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another such that the presence of both infected (peri)pancreatic necrosis and persistent organ failure have a greater effect on severity than either determinant alone. The derivation of a classification based on the above principles results in 4 categories of severity-mild, moderate, severe, and critical. This classification provides a set of concise up-to-date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research.

A Comparison Between a Synthetic Over-Sampling Equilibrium and Observed Subsets of Data for Epidemic Vector Classification

NASA Astrophysics Data System (ADS)

Fusco, Terence; Bi, Yaxin; Nugent, Chris; Wu, Shengli

2016-08-01

We can see that the data imputation approach using the Regression CTA has performed more favourably when compared with the alternative methods on this dataset. We now have the evidence to show that this method is viable moving forward with further research in this area. The weighted distribution experiments have provided us with a more balanced and appropriate ratio for snail density classification purposes when using either the 3 or 5 category combination. The most desirable results are found when using 3 categories of SD with the weighted distribution of classes being 20-60-20. This information reflects the optimum classification accuracy across the data range and can be applied to any novel environment feature dataset pertaining to Schistosomiasis vector classification. ITSVM has provided us with a method of labelling SD data which we can use for classification with epidemic disease prediction research. The confidence level selection enables consistent labelling accuracy for bespoke requirements when classifying the data from each year. The SMOTE Equilibrium proposed method has yielded a slight increase with each multiple of synthetic instances that are compounded to the training dataset. The reduction of overfitting and increase of data instances has shown a gradual classification accuracy increase across the data for each year. We will now test to see what the optimum synthetic instance incremental increase is across our data and apply this to our experiments with this research.

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Feasibility and validity of International Classification of Diseases based case mix indices.

PubMed

Yang, Che-Ming; Reinke, William

2006-10-06

Severity of illness is an omnipresent confounder in health services research. Resource consumption can be applied as a proxy of severity. The most commonly cited hospital resource consumption measure is the case mix index (CMI) and the best-known illustration of the CMI is the Diagnosis Related Group (DRG) CMI used by Medicare in the U.S. For countries that do not have DRG type CMIs, the adjustment for severity has been troublesome for either reimbursement or research purposes. The research objective of this study is to ascertain the construct validity of CMIs derived from International Classification of Diseases (ICD) in comparison with DRG CMI. The study population included 551 acute care hospitals in Taiwan and 2,462,006 inpatient reimbursement claims. The 18th version of GROUPER, the Medicare DRG classification software, was applied to Taiwan's 1998 National Health Insurance (NHI) inpatient claim data to derive the Medicare DRG CMI. The same weighting principles were then applied to determine the ICD principal diagnoses and procedures based costliness and length of stay (LOS) CMIs. Further analyses were conducted based on stratifications according to teaching status, accreditation levels, and ownership categories. The best ICD-based substitute for the DRG costliness CMI (DRGCMI) is the ICD principal diagnosis costliness CMI (ICDCMI-DC) in general and in most categories with Spearman's correlation coefficients ranging from 0.938-0.462. The highest correlation appeared in the non-profit sector. ICD procedure costliness CMI (ICDCMI-PC) outperformed ICDCMI-DC only at the medical center level, which consists of tertiary care hospitals and is more procedure intensive. The results of our study indicate that an ICD-based CMI can quite fairly approximate the DRGCMI, especially ICDCMI-DC. Therefore, substituting ICDs for DRGs in computing the CMI ought to be feasible and valid in countries that have not implemented DRGs.

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2014 CFR

2014-10-01

... discharge is based, as appropriate, on the patient's age, sex, principal diagnosis (that is, the diagnosis... whether a change in the DRG assignment is appropriate. If the intermediary decides that a higher-weighted... (b) of this section at least annually to reflect changes in treatment patterns, technology, and other...

Risk profiles for weight gain among postmenopausal women: A classification and regression tree analysis approach

USDA-ARS?s Scientific Manuscript database

Risk factors for obesity and weight gain are typically evaluated individually while "adjusting for" the influence of other confounding factors, and few studies, if any, have created risk profiles by clustering risk factors. We identified subgroups of postmenopausal women homogeneous in their cluster...

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

[Decreasing the Output of Biomedical Waste in the Intensive Care Unit].

PubMed

Shen, Ming-Yi; Chang, Chun-Chu; Li, Mung-Yeng; Lin, Jui-Hsiang

2017-10-01

Advancing healthcare technologies have increased the use of disposable supplies that are made with PVC (polyvinyl chloride). Furthermore, biomedical effluents are steadily increasing due to severe patient treatment requirements in intensive care units. If these biomedical wastes are not properly managed and disposed, they will cause great harm to the environment and to public health. The statistics from an intensive care unit at one medical center in northern Taiwan show that the per-person biomedical effluents produced in 2014 increased 8.51% over 2013 levels. The main reasons for this increase included the low accuracy of classification of the contents of biomedical effluent collection buckets and of personnel effluents in the intensive care unit and the generally poor selection and designation of appropriate containers. Improvement measures were implemented in order to decrease the per-day weight of biomedical effluents by 10% per person (-0.22 kg/person/day). The project team developed various strategies, including creating classification-related slogans and posting promotional posters, holding education and training using actual case studies, establishing an "environmental protection pioneer" team, and promoting the use of appropriate containers. The implementation of the project decreased the per-day weight of biomedical effluents by 13.2% per person. Implementation of the project effectively reduced the per-person daily output of biological wastes and improved the waste separation behavior of healthcare personnel in the unit, giving patients and their families a better healthcare environment and helping advance the cause of environmental protection worldwide.

The evaluation of lumbar paraspinal muscle quantity and quality using the Goutallier classification and lumbar indentation value.

PubMed

Tamai, Koji; Chen, Jessica; Stone, Michael; Arakelyan, Anush; Paholpak, Permsak; Nakamura, Hiroaki; Buser, Zorica; Wang, Jeffrey C

2018-05-01

The cross-sectional area and fat infiltration are accepted as standard parameters for quantitative and qualitative evaluation of muscle degeneration. However, they are time-consuming, which prevents them from being used in a clinical setting. The aim of this study was to analyze the relationship between lumbar muscle degeneration and spinal degenerative disorders, using lumbar indentation value (LIV) as quantitative and Goutallier classification as qualitative measures. This is a retrospective analysis of kinematic magnetic resonance images (kMRI). Two-hundred and thirty patients with kMRIs taken in weight-bearing positions were selected randomly. The LIV and Goutallier classification were evaluated at L4-5. The correlation of these two parameters with patients' age, gender, lumbar lordosis (LL), range of motion, disc degeneration, disc height, and Modic change were analyzed. There was no significant trend of LIV among the different grades of Goutallier classification (p = 0.943). There was a significant increase in age with higher grades of Goutallier classification (p < 0.001). In contrast, there was no correlation between LIV and age (p = 0.799). The Goutallier classification positively correlated with LL (r = 0.377) and severe disc degeneration (r = 0.249). The LIV positively correlated with LL (r = 0.476) and degenerative spondylolisthesis (r = 0.184). Multinomial logistic regression analysis showed that age (p = 0.026), gender (p = 0.003), and LIV (p < 0.001) were significant predictors for patients with low LL (< 10°). Lumbar muscle quantity and quality showed specific correlation with age and spine disorders. Additionally, LL can be predicted by the muscle quantity, but not the quality. These time-saving evaluation tools potentially accelerate the study of lumbar muscles. These slides can be retrieved under Electronic Supplementary Material.

2017 European League Against Rheumatism/American College of Rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups.

PubMed

Lundberg, Ingrid E; Tjärnlund, Anna; Bottai, Matteo; Werth, Victoria P; Pilkington, Clarissa; Visser, Marianne de; Alfredsson, Lars; Amato, Anthony A; Barohn, Richard J; Liang, Matthew H; Singh, Jasvinder A; Aggarwal, Rohit; Arnardottir, Snjolaug; Chinoy, Hector; Cooper, Robert G; Dankó, Katalin; Dimachkie, Mazen M; Feldman, Brian M; Torre, Ignacio Garcia-De La; Gordon, Patrick; Hayashi, Taichi; Katz, James D; Kohsaka, Hitoshi; Lachenbruch, Peter A; Lang, Bianca A; Li, Yuhui; Oddis, Chester V; Olesinska, Marzena; Reed, Ann M; Rutkowska-Sak, Lidia; Sanner, Helga; Selva-O'Callaghan, Albert; Song, Yeong-Wook; Vencovsky, Jiri; Ytterberg, Steven R; Miller, Frederick W; Rider, Lisa G

2017-12-01

To develop and validate new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIM) and their major subgroups. Candidate variables were assembled from published criteria and expert opinion using consensus methodology. Data were collected from 47 rheumatology, dermatology, neurology and paediatric clinics worldwide. Several statistical methods were used to derive the classification criteria. Based on data from 976 IIM patients (74% adults; 26% children) and 624 non-IIM patients with mimicking conditions (82% adults; 18% children), new criteria were derived. Each item is assigned a weighted score. The total score corresponds to a probability of having IIM. Subclassification is performed using a classification tree. A probability cut-off of 55%, corresponding to a score of 5.5 (6.7 with muscle biopsy) 'probable IIM', had best sensitivity/specificity (87%/82% without biopsies, 93%/88% with biopsies) and is recommended as a minimum to classify a patient as having IIM. A probability of ≥90%, corresponding to a score of ≥7.5 (≥8.7 with muscle biopsy), corresponds to 'definite IIM'. A probability of <50%, corresponding to a score of <5.3 (<6.5 with muscle biopsy), rules out IIM, leaving a probability of ≥50 to <55% as 'possible IIM'. The European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for IIM have been endorsed by international rheumatology, dermatology, neurology and paediatric groups. They employ easily accessible and operationally defined elements, and have been partially validated. They allow classification of 'definite', 'probable' and 'possible' IIM, in addition to the major subgroups of IIM, including juvenile IIM. They generally perform better than existing criteria. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

PubMed

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Understanding patient outcomes after acute respiratory distress syndrome: identifying subtypes of physical, cognitive and mental health outcomes.

PubMed

Brown, Samuel M; Wilson, Emily L; Presson, Angela P; Dinglas, Victor D; Greene, Tom; Hopkins, Ramona O; Needham, Dale M

2017-12-01

With improving short-term mortality in acute respiratory distress syndrome (ARDS), understanding survivors' posthospitalisation outcomes is increasingly important. However, little is known regarding associations among physical, cognitive and mental health outcomes. Identification of outcome subtypes may advance understanding of post-ARDS morbidities. We analysed baseline variables and 6-month health status for participants in the ARDS Network Long-Term Outcomes Study. After division into derivation and validation datasets, we used weighted network analysis to identify subtypes from predictors and outcomes in the derivation dataset. We then used recursive partitioning to develop a subtype classification rule and assessed adequacy of the classification rule using a kappa statistic with the validation dataset. Among 645 ARDS survivors, 430 were in the derivation and 215 in the validation datasets. Physical and mental health status, but not cognitive status, were closely associated. Four distinct subtypes were apparent (percentages in the derivation cohort): (1) mildly impaired physical and mental health (22% of patients), (2) moderately impaired physical and mental health (39%), (3) severely impaired physical health with moderately impaired mental health (15%) and (4) severely impaired physical and mental health (24%). The classification rule had high agreement (kappa=0.89 in validation dataset). Female Latino smokers had the poorest status, while male, non-Latino non-smokers had the best status. We identified four post-ARDS outcome subtypes that were predicted by sex, ethnicity, pre-ARDS smoking status and other baseline factors. These subtypes may help develop tailored rehabilitation strategies, including investigation of combined physical and mental health interventions, and distinct interventions to improve cognitive outcomes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Evaluation of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) classification scheme for diagnosis of cutaneous melanocytic neoplasms: Results from the International Melanoma Pathology Study Group.

PubMed

Lott, Jason P; Elmore, Joann G; Zhao, Ge A; Knezevich, Stevan R; Frederick, Paul D; Reisch, Lisa M; Chu, Emily Y; Cook, Martin G; Duncan, Lyn M; Elenitsas, Rosalie; Gerami, Pedram; Landman, Gilles; Lowe, Lori; Messina, Jane L; Mihm, Martin C; van den Oord, Joost J; Rabkin, Michael S; Schmidt, Birgitta; Shea, Christopher R; Yun, Sook Jung; Xu, George X; Piepkorn, Michael W; Elder, David E; Barnhill, Raymond L

2016-08-01

Pathologists use diverse terminology when interpreting melanocytic neoplasms, potentially compromising quality of care. We sought to evaluate the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) scheme, a 5-category classification system for melanocytic lesions. Participants (n = 16) of the 2013 International Melanoma Pathology Study Group Workshop provided independent case-level diagnoses and treatment suggestions for 48 melanocytic lesions. Individual diagnoses (including, when necessary, least and most severe diagnoses) were mapped to corresponding MPATH-Dx classes. Interrater agreement and correlation between MPATH-Dx categorization and treatment suggestions were evaluated. Most participants were board-certified dermatopathologists (n = 15), age 50 years or older (n = 12), male (n = 9), based in the United States (n = 11), and primary academic faculty (n = 14). Overall, participants generated 634 case-level diagnoses with treatment suggestions. Mean weighted kappa coefficients for diagnostic agreement after MPATH-Dx mapping (assuming least and most severe diagnoses, when necessary) were 0.70 (95% confidence interval 0.68-0.71) and 0.72 (95% confidence interval 0.71-0.73), respectively, whereas correlation between MPATH-Dx categorization and treatment suggestions was 0.91. This was a small sample size of experienced pathologists in a testing situation. Varying diagnostic nomenclature can be classified into a concise hierarchy using the MPATH-Dx scheme. Further research is needed to determine whether this classification system can facilitate diagnostic concordance in general pathology practice and improve patient care. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Regional brain volumetry and brain function in severely brain-injured patients.

PubMed

Annen, Jitka; Frasso, Gianluca; Crone, Julia Sophia; Heine, Lizette; Di Perri, Carol; Martial, Charlotte; Cassol, Helena; Demertzi, Athena; Naccache, Lionel; Laureys, Steven

2018-04-01

The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853. © 2018 American Neurological Association.

[International multidisciplinary classification of acute pancreatitis severity: the 2013 Spanish edition].

PubMed

Maraví-Poma, E; Patchen Dellinger, E; Forsmark, C E; Layer, P; Lévy, P; Shimosegawa, T; Siriwardena, A K; Uomo, G; Whitcomb, D C; Windsor, J A; Petrov, M S

2014-05-01

To develop a new classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of the published evidence, and worldwide consultation. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of specialist in pancreatic diseases, but are suboptimal because these definitions are based on the empiric description of events not associated with severity. A personal invitation to contribute to the development of a new classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists and radiologists currently active in the field of clinical acute pancreatitis. The invitation was not limited to members of certain associations or residents of certain countries. A global web-based survey was conducted, and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new classification of severity is based on the actual local and systemic determinants of severity, rather than on the description of events that are non-causally associated with severity. The local determinant relates to whether there is (peri) pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another, whereby the presence of both infected (peri) pancreatic necrosis and persistent organ failure has a greater impact upon severity than either determinant alone. The derivation of a classification based on the above principles results in four categories of severity: mild, moderate, severe, and critical. This classification is the result of a consultative process among specialists in pancreatic diseases from 49 countries spanning North America, South America, Europe, Asia, Oceania and Africa. It provides a set of concise up to date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research. This ensures that the determinant-based classification can be used in a uniform manner throughout the world. Copyright © 2013 Elsevier España, S.L. and SEMICYUC. All rights reserved.

MUSAC II: A Method for Modeling Passive Sonar Classification in a Multiple Target Environment.

DTIC Science & Technology

1976-02-01

TERAIN #46-6667 - Green (chlorophyll Type) (Parts by Weight) Cantor Wax 21.6 Carnauba Wax 3.1 Mineral Oil, U.S.P.Heavy 22.5 lanolin, U.S.P. angdrous 10.5...MMOSITICt - PAINT, FACE, CAMOUF1AGE, ARID TURAIN #23-6667-Lom #21-6667-Sand (Parts by weight) (Parts by weight) Castor Wax 17.5 17.5 CartAuba Wax 2.5

Genetic parameters for type classification of Nelore cattle on central performance tests at pasture in Brazil.

PubMed

Lima, Paulo Ricardo Martins; Paiva, Samuel Rezende; Cobuci, Jaime Araujo; Braccini Neto, José; Machado, Carlos Henrique Cavallari; McManus, Concepta

2013-10-01

The objective of this study was to characterize Nelore cattle on central performance tests in pasture, ranked by the visual classification method EPMURAS (structure, precocity, muscle, navel, breed, posture, and sexual characteristics), and to estimate genetic and phenotypic correlations between these parameters, including visual as well as production traits (initial and final weight on test, weight gain, and weight corrected for 550 days). The information used in the study was obtained on 21,032 Nelore bulls which were participants in the central performance test at pasture of the Brazilian Association for Zebu Breeders (ABCZ). Heritabilities obtained were from 0.19 to 0.50. Phenotypic correlations were positive from 0.70 to 0.97 between the weight traits, from 0.65 to 0.74 between visual characteristics, and from 0.29 to 0.47 between visual characteristics and weight traits. The genetic correlations were positive ranging from 0.80 to 0.98 between the characteristics of structure, precocity and musculature, from 0.13 to 0.64 between the growth characteristics, and from 0.41 to 0.97 between visual scores and weight gains. Heritability and genetic correlations indicate that the use of visual scores, along with the selection for growth characteristics, can bring positive results in selection of beef cattle for rearing on pasture.

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Systemic Sclerosis Classification Criteria: Developing methods for multi-criteria decision analysis with 1000Minds

PubMed Central

Johnson, Sindhu R.; Naden, Raymond P.; Fransen, Jaap; van den Hoogen, Frank; Pope, Janet E.; Baron, Murray; Tyndall, Alan; Matucci-Cerinic, Marco; Denton, Christopher P.; Distler, Oliver; Gabrielli, Armando; van Laar, Jacob M.; Mayes, Maureen; Steen, Virginia; Seibold, James R.; Clements, Phillip; Medsger, Thomas A.; Carreira, Patricia E.; Riemekasten, Gabriela; Chung, Lorinda; Fessler, Barri J.; Merkel, Peter A.; Silver, Richard; Varga, John; Allanore, Yannick; Mueller-Ladner, Ulf; Vonk, Madelon C.; Walker, Ulrich A.; Cappelli, Susanna; Khanna, Dinesh

2014-01-01

Objective Classification criteria for systemic sclerosis (SSc) are being developed. The objectives were to: develop an instrument for collating case-data and evaluate its sensibility; use forced-choice methods to reduce and weight criteria; and explore agreement between experts on the probability that cases were classified as SSc. Study Design and Setting A standardized instrument was tested for sensibility. The instrument was applied to 20 cases covering a range of probabilities that each had SSc. Experts rank-ordered cases from highest to lowest probability; reduced and weighted the criteria using forced-choice methods; and re-ranked the cases. Consistency in rankings was evaluated using intraclass correlation coefficients (ICC). Results Experts endorsed clarity (83%), comprehensibility (100%), face and content validity (100%). Criteria were weighted (points): finger skin thickening (14–22), finger-tip lesions (9–21), friction rubs (21), finger flexion contractures (16), pulmonary fibrosis (14), SSc-related antibodies (15), Raynaud’s phenomenon (13), calcinosis (12), pulmonary hypertension (11), renal crisis (11), telangiectasia (10), abnormal nailfold capillaries (10), esophageal dilation (7) and puffy fingers (5). The ICC across experts was 0.73 (95%CI 0.58,0.86) and improved to 0.80 (95%CI 0.68,0.90). Conclusions Using a sensible instrument and forced-choice methods, the number of criteria were reduced by 39% (23 to 14) and weighted. Our methods reflect the rigors of measurement science, and serves as a template for developing classification criteria. PMID:24721558

Classification and identification of molecules through factor analysis method based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Jianglou; Liu, Jinsong; Wang, Kejia; Yang, Zhengang; Liu, Xiaming

2018-06-01

By means of factor analysis approach, a method of molecule classification is built based on the measured terahertz absorption spectra of the molecules. A data matrix can be obtained by sampling the absorption spectra at different frequency points. The data matrix is then decomposed into the product of two matrices: a weight matrix and a characteristic matrix. By using the K-means clustering to deal with the weight matrix, these molecules can be classified. A group of samples (spirobenzopyran, indole, styrene derivatives and inorganic salts) has been prepared, and measured via a terahertz time-domain spectrometer. These samples are classified with 75% accuracy compared to that directly classified via their molecular formulas.

Using existing case-mix methods to fund trauma cases.

PubMed

Monakova, Julia; Blais, Irene; Botz, Charles; Chechulin, Yuriy; Picciano, Gino; Basinski, Antoni

2010-01-01

Policymakers frequently face the need to increase funding in isolated and frequently heterogeneous (clinically and in terms of resource consumption) patient subpopulations. This article presents a methodologic solution for testing the appropriateness of using existing grouping and weighting methodologies for funding subsets of patients in the scenario where a case-mix approach is preferable to a flat-rate based payment system. Using as an example the subpopulation of trauma cases of Ontario lead trauma hospitals, the statistical techniques of linear and nonlinear regression models, regression trees, and spline models were applied to examine the fit of the existing case-mix groups and reference weights for the trauma cases. The analyses demonstrated that for funding Ontario trauma cases, the existing case-mix systems can form the basis for rational and equitable hospital funding, decreasing the need to develop a different grouper for this subset of patients. This study confirmed that Injury Severity Score is a poor predictor of costs for trauma patients. Although our analysis used the Canadian case-mix classification system and cost weights, the demonstrated concept of using existing case-mix systems to develop funding rates for specific subsets of patient populations may be applicable internationally.

Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification.

PubMed

Pozo-Aguilar, Jorge O; Monroy-Martínez, Verónica; Díaz, Daniel; Barrios-Palacios, Jacqueline; Ramos, Celso; Ulloa-García, Armando; García-Pillado, Janet; Ruiz-Ordaz, Blanca H

2014-12-11

Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification. A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated. The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur. The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009 WHO classification showed greater sensitivity to identify SD in real time. Timely identification of SD enables accurate early decisions, allowing proper management of health resources for the benefit of patients at risk for SD. This is possible based on the 2009 WHO classification.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach.

PubMed

Mouriño García, Marcos Antonio; Pérez Rodríguez, Roberto; Anido Rifón, Luis E

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text-thus suffering from synonymy and polysemy-and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge-concretely Wikipedia-in order to create bag-of-concepts (BoC) representations of documents, understanding concept as "unit of meaning", and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach

PubMed Central

Pérez Rodríguez, Roberto; Anido Rifón, Luis E.

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria—that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text—thus suffering from synonymy and polysemy—and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge—concretely Wikipedia—in order to create bag-of-concepts (BoC) representations of documents, understanding concept as “unit of meaning”, and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus. PMID:26468436

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.

PubMed

Duraisamy, Baskar; Shanmugam, Jayanthi Venkatraman; Annamalai, Jayanthi

2018-02-19

An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems.

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

NASA Technical Reports Server (NTRS)

Abbey, Craig K.; Eckstein, Miguel P.

2002-01-01

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Assessment of reproductive and developmental effects of DINP, DnHP and DCHP using quantitative weight of evidence.

PubMed

Dekant, Wolfgang; Bridges, James

2016-11-01

Quantitative weight of evidence (QWoE) methodology utilizes detailed scoring sheets to assess the quality/reliability of each publication on toxicity of a chemical and gives numerical scores for quality and observed toxicity. This QWoE-methodology was applied to the reproductive toxicity data on diisononylphthalate (DINP), di-n-hexylphthalate (DnHP), and dicyclohexylphthalate (DCHP) to determine if the scientific evidence for adverse effects meets the requirements for classification as reproductive toxicants. The scores for DINP were compared to those when applying the methodology DCHP and DnHP that have harmonized classifications. Based on the quality/reliability scores, application of the QWoE shows that the three databases are of similar quality; but effect scores differ widely. Application of QWoE to DINP studies resulted in an overall score well below the benchmark required to trigger classification. For DCHP, the QWoE also results in low scores. The high scores from the application of the QWoE methodology to the toxicological data for DnHP represent clear evidence for adverse effects and justify a classification of DnHP as category 1B for both development and fertility. The conclusions on classification based on the QWoE are well supported using a narrative assessment of consistency and biological plausibility. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Weight classification does not influence the short-term endocrine or metabolic effects of high-fructose corn syrup-sweetened beverages.

PubMed

Heden, Timothy D; Liu, Ying; Kearney, Monica L; Kanaley, Jill A

2014-05-01

Obesity and high-fructose corn syrup (HFCS)-sweetened beverages are associated with an increased risk of chronic disease, but it is not clear whether obese (Ob) individuals are more susceptible to the detrimental effects of HFCS-sweetened beverages. The purpose of this study was to examine the endocrine and metabolic effects of consuming HFCS-sweetened beverages, and whether weight classification (normal weight (NW) vs. Ob) influences these effects. Ten NW and 10 Ob men and women who habitually consumed ≤355 mL per day of sugar-sweetened beverages were included in this study. Initially, the participants underwent a 4-h mixed-meal test after a 12-h overnight fast to assess insulin sensitivity, pancreatic and gut endocrine responses, insulin secretion and clearance, and glucose, triacylglycerol, and cholesterol responses. Next, the participants consumed their normal diet ad libitum, with 1065 mL per day (117 g·day(-1)) of HFCS-sweetened beverages added for 2 weeks. After the intervention, the participants repeated the mixed-meal test. HFCS-sweetened beverages did not significantly alter body weight, insulin sensitivity, insulin secretion or clearance, or endocrine, glucose, lipid, or cholesterol responses in either NW or Ob individuals. Regardless of previous diet, Ob individuals, compared with NW individuals, had ∼28% lower physical activity levels, 6%-9% lower insulin sensitivity, 12%-16% lower fasting high-density-lipoprotein cholesterol concentrations, 84%-144% greater postprandial triacylglycerol concentrations, and 46%-79% greater postprandial insulin concentrations. Greater insulin responses were associated with reduced insulin clearance, and there were no differences in insulin secretion. These findings suggest that weight classification does not influence the short-term endocrine and metabolic effects of HFCS-sweetened beverages.

Voice classification and vocal tract of singers: a study of x-ray images and morphology.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-01-01

This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach.

PubMed

Ecker, Christine; Marquand, Andre; Mourão-Miranda, Janaina; Johnston, Patrick; Daly, Eileen M; Brammer, Michael J; Maltezos, Stefanos; Murphy, Clodagh M; Robertson, Dene; Williams, Steven C; Murphy, Declan G M

2010-08-11

Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating patterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e.g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.

A new classification scheme of plastic wastes based upon recycling labels

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

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr; Ergin, Semih, E-mail: sergin@ogu.edu.tr; Işık, Şahin, E-mail: sahini@ogu.edu.tr

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize thesemore » materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.« less

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

PubMed Central

Fu, Longsheng; Sun, Shipeng; Li, Rui; Wang, Shaojin

2016-01-01

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. PMID:27376292

76 FR 66931 - Medicare Program: Notice of Two Membership Appointments to the Advisory Panel on Ambulatory...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-28

... Administrator) concerning the clinical integrity of the APC groups and their weights. The advice provided by the Panel will be considered as CMS prepares its annual updates of the hospital outpatient prospective... clinical integrity of the Ambulatory Payment Classification (APC) groups and their associated weights. The...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2010 CFR

2010-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2012 CFR

2012-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

7 CFR 51.1437 - Size classifications for halves.

Code of Federal Regulations, 2011 CFR

2011-01-01

... weight of half-kernels after all pieces, particles and dust, shell, center wall, and foreign material..., particles, and dust. In order to allow for variations incident to proper sizing and handling, not more than 15 percent, by weight, of any lot may consist of pieces, particles, and dust: Provided, That not more...

Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification.

PubMed

Borozan, Ivan; Watt, Stuart; Ferretti, Vincent

2015-05-01

Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences. All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html. ivan.borozan@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification

PubMed Central

Borozan, Ivan; Watt, Stuart; Ferretti, Vincent

2015-01-01

Motivation: Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized. Results: Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences. Availability and implementation: All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html. Contact: ivan.borozan@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25573913

The method for froth floatation condition recognition based on adaptive feature weighted

NASA Astrophysics Data System (ADS)

Wang, Jieran; Zhang, Jun; Tian, Jinwen; Zhang, Daimeng; Liu, Xiaomao

2018-03-01

The fusion of foam characteristics can play a complementary role in expressing the content of foam image. The weight of foam characteristics is the key to make full use of the relationship between the different features. In this paper, an Adaptive Feature Weighted Method For Froth Floatation Condition Recognition is proposed. Foam features without and with weights are both classified by using support vector machine (SVM).The classification accuracy and optimal equaling algorithm under the each ore grade are regarded as the result of the adaptive feature weighting algorithm. At the same time the effectiveness of adaptive weighted method is demonstrated.

Grading Dysphagia as a Toxicity of Head and Neck Cancer: Differences in Severity Classification Based on MBS DIGEST and Clinical CTCAE Grades.

PubMed

Goepfert, Ryan P; Lewin, Jan S; Barrow, Martha P; Warneke, Carla L; Fuller, Clifton D; Lai, Stephen Y; Weber, Randal S; Hutcheson, Katherine A

2018-04-01

Clinician-reported toxicity grading through common terminology criteria for adverse events (CTCAE) stages dysphagia based on symptoms, diet, and tube dependence. The new dynamic imaging grade of swallowing toxicity (DIGEST) tool offers a similarly scaled five-point ordinal summary grade of pharyngeal swallowing as determined through results of a modified barium swallow (MBS) study. This study aims to inform clinicians on the similarities and differences between dysphagia severity according to clinical CTCAE and MBS-derived DIGEST grading. A cross-sectional sample of 95 MBS studies was randomly selected from a prospectively-acquired MBS database among patients treated with organ preservation strategies for head and neck cancer. MBS DIGEST and clinical CTCAE dysphagia grades were compared. DIGEST and CTCAE dysphagia grades had "fair" agreement per weighted κ of 0.358 (95% CI .231-.485). Using a threshold of DIGEST ≥ 3 as reference, CTCAE had an overall sensitivity of 0.50, specificity of 0.84, and area under the curve (AUC) of 0.67 to identify severe MBS-detected dysphagia. At less than 6 months, sensitivity was 0.72, specificity was 0.76, and AUC was 0.75 while at greater than 6 months, sensitivity was 0.22, specificity was 0.90, and AUC was 0.56 for CTCAE to detect dysphagia as determined by DIGEST. Classification of pharyngeal dysphagia on MBS using DIGEST augments our understanding of dysphagia severity according to the clinically-derived CTCAE while maintaining the simplicity of an ordinal scale. DIGEST likely complements CTCAE toxicity grading through improved specificity for physiologic dysphagia in the acute phase and improved sensitivity for dysphagia in the late-phase.

Comparison of six fire severity classification methods using Montana and Washington wildland fires

Treesearch

Pamela G. Sikkink

2015-01-01

Fire severity classifications are used in the post-fire environment to describe fire effects, such as soil alteration or fuel consumption, on the forest floor. Most of the developed classifications are limited because they address very specific fire effects or post-burn characteristics in the burned environment. However, because fire effects vary so much among soil,...

Evaluation of Short-Term Cepstral Based Features for Detection of Parkinson’s Disease Severity Levels through Speech signals

NASA Astrophysics Data System (ADS)

Oung, Qi Wei; Nisha Basah, Shafriza; Muthusamy, Hariharan; Vijean, Vikneswaran; Lee, Hoileong

2018-03-01

Parkinson’s disease (PD) is one type of progressive neurodegenerative disease known as motor system syndrome, which is due to the death of dopamine-generating cells, a region of the human midbrain. PD normally affects people over 60 years of age, which at present has influenced a huge part of worldwide population. Lately, many researches have shown interest into the connection between PD and speech disorders. Researches have revealed that speech signals may be a suitable biomarker for distinguishing between people with Parkinson’s (PWP) from healthy subjects. Therefore, early diagnosis of PD through the speech signals can be considered for this aim. In this research, the speech data are acquired based on speech behaviour as the biomarker for differentiating PD severity levels (mild and moderate) from healthy subjects. Feature extraction algorithms applied are Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC). For classification, two types of classifiers are used: k-Nearest Neighbour (KNN) and Probabilistic Neural Network (PNN). The experimental results demonstrated that PNN classifier and KNN classifier achieve the best average classification performance of 92.63% and 88.56% respectively through 10-fold cross-validation measures. Favourably, the suggested techniques have the possibilities of becoming a new choice of promising tools for the PD detection with tremendous performance.

RBOOST: RIEMANNIAN DISTANCE BASED REGULARIZED BOOSTING

PubMed Central

Liu, Meizhu; Vemuri, Baba C.

2011-01-01

Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost, LPBoost, SoftBoost and their variations. However, the learning update strategies used in these methods usually lead to overfitting and instabilities in the classification accuracy. Improved boosting methods via regularization can overcome such difficulties. In this paper, we propose a Riemannian distance regularized LPBoost, dubbed RBoost. RBoost uses Riemannian distance between two square-root densities (in closed form) – used to represent the distribution over the training data and the classification error respectively – to regularize the error distribution in an iterative update formula. Since this distance is in closed form, RBoost requires much less computational cost compared to other regularized Boosting algorithms. We present several experimental results depicting the performance of our algorithm in comparison to recently published methods, LP-Boost and CAVIAR, on a variety of datasets including the publicly available OASIS database, a home grown Epilepsy database and the well known UCI repository. Results depict that the RBoost algorithm performs better than the competing methods in terms of accuracy and efficiency. PMID:21927643

GHS additivity formula: A true replacement method for acute systemic toxicity testing of agrochemical formulations.

PubMed

Corvaro, M; Gehen, S; Andrews, K; Chatfield, R; Arasti, C; Mehta, J

2016-12-01

Acute systemic (oral, dermal, inhalation) toxicity testing of agrochemical formulations (end-use products) is mainly needed for Classification and Labelling (C&L) and definition of personal protection equipment (PPE). A retrospective analysis of 225 formulations with available in vivo data showed that: A) LD 50 /LC 50 values were above limit doses in <20.2% via oral route but only in <1% and <2.4% of cases via dermal and inhalation route, respectively; B) for each formulation the acute oral toxicity is always equal or greater than the Acute Toxicity Estimate (ATE) via the other two routes; C) the GHS (Global Harmonised System) computational method based on ATE, currently of limited acceptance, has very high accuracy and specificity for prediction of agrochemical mixture toxicity according to the internationally established classification thresholds. By integrating this evidence, an exposure- and data-based waiving strategy is proposed to determine classification and adequate PPE and to ensure only triggered animal testing is used. Safety characterisation above 2000 mg/kg body weight or 1.0 mg/L air should not be recommended, based on the agrochemical exposure scenarios. The global implementation of these tools would allow a remarkable reduction (up to 95%) in in vivo testing, often inducing lethality and/or severe toxicity, for agrochemical formulations. Copyright © 2016. Published by Elsevier Inc.

Gender classification under extended operating conditions

NASA Astrophysics Data System (ADS)

Rude, Howard N.; Rizki, Mateen

2014-06-01

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

What makes children with cerebral palsy vulnerable to malnutrition? Findings from the Bangladesh cerebral palsy register (BCPR).

PubMed

Jahan, Israt; Muhit, Mohammad; Karim, Tasneem; Smithers-Sheedy, Hayley; Novak, Iona; Jones, Cheryl; Badawi, Nadia; Khandaker, Gulam

2018-04-16

To assess the nutritional status and underlying risk factors for malnutrition among children with cerebral palsy in rural Bangladesh. We used data from the Bangladesh Cerebral Palsy Register; a prospective population based surveillance of children with cerebral palsy aged 0-18 years in a rural subdistrict of Bangladesh (i.e., Shahjadpur). Socio-demographic, clinical and anthropometric measurements were collected using Bangladesh Cerebral Palsy Register record form. Z scores were calculated using World Health Organization Anthro and World Health Organization AnthroPlus software. A total of 726 children with cerebral palsy were registered into the Bangladesh Cerebral Palsy Register (mean age 7.6 years, standard deviation 4.5, 38.1% female) between January 2015 and December 2016. More than two-third of children were underweight (70.0%) and stunted (73.1%). Mean z score for weight for age, height for age and weight for height were -2.8 (standard deviation 1.8), -3.1 (standard deviation 2.2) and -1.2 (standard deviation 2.3) respectively. Moderate to severe undernutrition (i.e., both underweight and stunting) were significantly associated with age, monthly family income, gross motor functional classification system and neurological type of cerebral palsy. The burden of undernutrition is high among children with cerebral palsy in rural Bangladesh which is augmented by both poverty and clinical severity. Enhancing clinical nutritional services for children with cerebral palsy should be a public health priority in Bangladesh. Implications for Rehabilitation Population-based surveillance data on nutritional status of children with cerebral palsy in Bangladesh indicates substantially high burden of malnutrition among children with CP in rural Bangladesh. Children with severe form of cerebral palsy, for example, higher Gross Motor Function Classification System (GMFCS) level, tri/quadriplegic cerebral palsy presents the highest proportion of severe malnutrition; hence, these vulnerable groups should be focused in designing nutrition intervention and rehabilitation programs. Disability inclusive and focused nutrition intervention programme need to be kept as priority in national nutrition policies and nutrition action plans specially in low- and middle-income countries. Community-based management of malnutrition has the potential to overcome this poor nutritional scenario of children with disability (i.e., cerebral palsy). The global leaders such as World Health Organization, national and international organizations should take this in account and conduct further research to develop nutritional guidelines for this vulnerable group of population.

Children's weight changes according to maternal perception of the child's weight and health: A prospective cohort of Peruvian children.

PubMed

Carrillo-Larco, Rodrigo M; Bernabe-Ortiz, Antonio; Miranda, J Jaime; Xue, Hong; Wang, Youfa

2017-01-01

The aim of the study was to estimate the association between maternal perception of their child's health status and (mis)classification of their child's actual weight with future weight change. We present cross-sectional and longitudinal analyses from the Peruvian younger cohort of the Young Lives Study. For cross-sectional analysis, the exposure was maternal perception of child health status (better, same or worse); the outcome was underestimation or overestimation of the child's actual weight. Mothers were asked about their perception of their child's weight (same, lighter or heavier than other children). Actual weight status was defined with IOTF BMI cut-off points. For longitudinal analysis, the exposure was (mis)classification of the child's actual weight; the outcome was the standardized mean difference between follow-up and baseline BMI. A Generalized Linear Model with Poisson family and log-link was used to report the prevalence ratio (PR) and 95% confidence intervals (95% CI) for cross-sectional analyses. A Linear Regression Model was used to report the longitudinal analysis as coefficient estimates (β) and 95% CI. Normal weight children who were perceived as more healthy than other children were more likely to have their weight overestimated (PR = 2.06); conversely, those who were perceived as less healthy than other children were more likely to have their weight underestimated (PR = 2.17). Mean follow-up time was 2.6 (SD: 0.3) years. Overall, underweight children whose weight was overestimated were more likely to gain BMI (β = 0.44); whilst overweight children whose weight was considered to be the same of their peers (β = -0.55), and those considered to be lighter than other children (β = -0.87), lost BMI. Maternal perception of the child's health status seems to influence both overestimation and underestimation of the child's actual weight status. Such weight (mis)perception may influence future BMI.

Children’s weight changes according to maternal perception of the child’s weight and health: A prospective cohort of Peruvian children

PubMed Central

Carrillo-Larco, Rodrigo M.; Bernabe-Ortiz, Antonio; Miranda, J. Jaime; Xue, Hong; Wang, Youfa

2017-01-01

The aim of the study was to estimate the association between maternal perception of their child’s health status and (mis)classification of their child’s actual weight with future weight change. We present cross-sectional and longitudinal analyses from the Peruvian younger cohort of the Young Lives Study. For cross-sectional analysis, the exposure was maternal perception of child health status (better, same or worse); the outcome was underestimation or overestimation of the child’s actual weight. Mothers were asked about their perception of their child’s weight (same, lighter or heavier than other children). Actual weight status was defined with IOTF BMI cut-off points. For longitudinal analysis, the exposure was (mis)classification of the child’s actual weight; the outcome was the standardized mean difference between follow-up and baseline BMI. A Generalized Linear Model with Poisson family and log-link was used to report the prevalence ratio (PR) and 95% confidence intervals (95% CI) for cross-sectional analyses. A Linear Regression Model was used to report the longitudinal analysis as coefficient estimates (β) and 95% CI. Normal weight children who were perceived as more healthy than other children were more likely to have their weight overestimated (PR = 2.06); conversely, those who were perceived as less healthy than other children were more likely to have their weight underestimated (PR = 2.17). Mean follow-up time was 2.6 (SD: 0.3) years. Overall, underweight children whose weight was overestimated were more likely to gain BMI (β = 0.44); whilst overweight children whose weight was considered to be the same of their peers (β = -0.55), and those considered to be lighter than other children (β = -0.87), lost BMI. Maternal perception of the child’s health status seems to influence both overestimation and underestimation of the child’s actual weight status. Such weight (mis)perception may influence future BMI. PMID:28422975

Obesity increases the prevalence and the incidence of asthma and worsens asthma severity.

PubMed

Barros, R; Moreira, P; Padrão, P; Teixeira, V H; Carvalho, P; Delgado, L; Moreira, A

2017-08-01

We aimed to explore the association between obesity and asthma prevalence, incidence and severity. The study included 32,644 adults, 52.6% female, from a representative sample of the 4th Portuguese National Health Survey. The following asthma definitions were used: ever asthma (ever medical doctor asthma diagnosis), current asthma (asthma within the last 12 months), current persistent asthma (required asthma medication within the last 12 months), current severe asthma (attending an emergency department because of asthma within the last 12 months), and incident asthma (asthma diagnosis within the last 12 months). Body mass index was calculated based on self-reported weight and height and categorised according to WHO classification. Logistic regression models adjusted for confounders were performed. Prevalence of ever asthma was 5.3%, current asthma 3.5%, current persistent asthma 3.0%, current severe asthma 1.4%, and incident asthma 0.2%. Prevalence of obesity was 16%, overweight 37.6%, normal weight 44.6% and underweight 0.2%. Being overweight, obesity class I and II, and obesity class III were associated with an OR (95% CI) with ever asthma 1.22 (1.21-1.24), 1.39 (1.36-1.41), 3.24 (3.08-3.40) respectively; current asthma 1.16 (1.14-1.18), 1.86 (1.82-1.90), 4.73 (4.49-4.98) respectively; current persistent asthma 1.08 (1.06-1.10), 2.06 (2.01-2.10), 5.24 (4.96-5.53), and current severe asthma 1.36 (1.32-1.40), 1.50 (1.45-1.55) and 3.70 (3.46-3.95), respectively. Considering the incidence of asthma, obesity more than quadrupled the odds (OR = 4.46, 95% CI 4.30, 4.62). Obesity is associated in a dose dependent way with an increase of prevalent and incident asthma, and it seems to increase the odds of a more persistent and severe asthma phenotype independently of socio-demographic determinants, physical activity, and dietary patterns. Our results provide rational for future lifestyle intervention studies for weight reduction in the obesity-asthma phenotype. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Postoperative Delirium in Severely Burned Patients Undergoing Early Escharotomy: Incidence, Risk Factors, and Outcomes.

PubMed

Guo, Zhenggang; Liu, Jiabin; Li, Jia; Wang, Xiaoyan; Guo, Hui; Ma, Panpan; Su, Xiaojun; Li, Ping

The aim of this study is to investigate the incidence, related risk factors, and outcomes of postoperative delirium (POD) in severely burned patients undergoing early escharotomy. This study included 385 severely burned patients (injured <1 week; TBSA, 31-50% or 11-20%; American Society of Anesthesiologists physical status, II-IV) aged 18 to 65 years, who underwent early escharotomy between October 2014 and December 2015, and were selected by cluster sampling. The authors excluded patients with preoperative delirium or diagnosed dementia, depression, or cognitive dysfunction. Preoperative, perioperative, intraoperative, and postoperative information, such as demographic characteristics, vital signs, and health history were collected. The Confusion Assessment Method was used once daily for 5 days after surgery to identify POD. Stepwise binary logistic regression analysis was used to identify the risk factors for POD, t-tests, and χ tests were performed to compare the outcomes of patients with and without the condition. Fifty-six (14.55%) of the patients in the sample were diagnosed with POD. Stepwise binary logistic regression showed that the significant risk factors for POD in severely burned patients undergoing early escharotomy were advanced age (>50 years old), a history of alcohol consumption (>3/week), high American Society of Anesthesiologists classification (III or IV), time between injury and surgery (>2 days), number of previous escharotomies (>2), combined intravenous and inhalation anesthesia, no bispectral index applied, long duration surgery (>180 min), and intraoperative hypotension (mean arterial pressure < 55 mm Hg). On the basis of the different odds ratios, the authors established a weighted model. When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). Further, POD was associated with more postoperative complications, including hepatic and renal function impairment and hypernatremia, as well as prolonged hospitalization, increased medical costs, and higher mortality.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine.

Vertebral degenerative disc disease severity evaluation using random forest classification

NASA Astrophysics Data System (ADS)

Munoz, Hector E.; Yao, Jianhua; Burns, Joseph E.; Pham, Yasuyuki; Stieger, James; Summers, Ronald M.

2014-03-01

Degenerative disc disease (DDD) develops in the spine as vertebral discs degenerate and osseous excrescences or outgrowths naturally form to restabilize unstable segments of the spine. These osseous excrescences, or osteophytes, may progress or stabilize in size as the spine reaches a new equilibrium point. We have previously created a CAD system that detects DDD. This paper presents a new system to determine the severity of DDD of individual vertebral levels. This will be useful to monitor the progress of developing DDD, as rapid growth may indicate that there is a greater stabilization problem that should be addressed. The existing DDD CAD system extracts the spine from CT images and segments the cortical shell of individual levels with a dual-surface model. The cortical shell is unwrapped, and is analyzed to detect the hyperdense regions of DDD. Three radiologists scored the severity of DDD of each disc space of 46 CT scans. Radiologists' scores and features generated from CAD detections were used to train a random forest classifier. The classifier then assessed the severity of DDD at each vertebral disc level. The agreement between the computer severity score and the average radiologist's score had a quadratic weighted Cohen's kappa of 0.64.

Weight Status in the First 2 Years of Life and Neurodevelopmental Impairment in Extremely Low Gestational Age Newborns.

PubMed

Belfort, Mandy B; Kuban, Karl C K; O'Shea, T Michael; Allred, Elizabeth N; Ehrenkranz, Richard A; Engelke, Stephen C; Leviton, Alan

2016-01-01

To examine the extent to which weight gain and weight status in the first 2 years of life relate to the risk of neurodevelopmental impairment in extremely preterm infants. In a cohort of 1070 infants born between 23 and 27 weeks' gestation, we examined weight gain from 7-28 days of life (in quartiles) and weight z-score at 12 and 24 months corrected age (in 4 categories: <-2; ≥-2, <-1; ≥1, <1; and ≥1) in relation to these adverse neurodevelopmental outcomes: Bayley-II mental development index <55, Bayley-II psychomotor development index <55, cerebral palsy, Gross Motor Function Classification System ≥1 (cannot walk without assistance), microcephaly. We adjusted for confounders in logistic regression, stratified by sex, and performed separate analyses including the entire sample, and excluding children unable to walk without assistance (motor impairment). Weight gain in the lowest quartile from 7-28 days was not associated with higher risk of adverse outcomes. Children with a 12-month weight z-score <-2 were at increased risk for all adverse outcomes in girls, and for microcephaly and Gross Motor Function Classification System ≥1 in boys. However, excluding children with motor impairment attenuated all associations except that of weight z-score <-2 with microcephaly in girls. Similarly, most associations of low weight z-score at 24 months with adverse outcomes were attenuated with exclusion of children with motor impairment. Excluding children who have gross motor impairment appears to eliminate the association of low weight status with neurodevelopmental impairments at 2 years in extremely preterm infants. Copyright © 2016 Elsevier Inc. All rights reserved.

Rank-based pooling for deep convolutional neural networks.

PubMed

Shi, Zenglin; Ye, Yangdong; Wu, Yunpeng

2016-11-01

Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values. In this work, we alternatively propose rank-based pooling. It is derived from the observations that ranking list is invariant under changes of activation values in a pooling region, and thus rank-based pooling operation may achieve more robust performance. In addition, the reasonable usage of rank can avoid the scale problems encountered by value-based methods. The novel pooling mechanism can be regarded as an instance of weighted pooling where a weighted sum of activations is used to generate the pooling output. This pooling mechanism can also be realized as rank-based average pooling (RAP), rank-based weighted pooling (RWP) and rank-based stochastic pooling (RSP) according to different weighting strategies. As another major contribution, we present a novel criterion to analyze the discriminant ability of various pooling methods, which is heavily under-researched in machine learning and computer vision community. Experimental results on several image benchmarks show that rank-based pooling outperforms the existing pooling methods in classification performance. We further demonstrate better performance on CIFAR datasets by integrating RSP into Network-in-Network. Copyright © 2016 Elsevier Ltd. All rights reserved.

Estimation and classification by sigmoids based on mutual information

NASA Technical Reports Server (NTRS)

Baram, Yoram

1994-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

State-Level School Competitive Food and Beverage Laws Are Associated with Children's Weight Status

ERIC Educational Resources Information Center

Hennessy, Erin; Oh, April; Agurs-Collins, Tanya; Chriqui, Jamie F.; Mâsse, Louise C.; Moser, Richard P.; Perna, Frank

2014-01-01

Background: This study attempted to determine whether state laws regulating low nutrient, high energy-dense foods and beverages sold outside of the reimbursable school meals program (referred to as "competitive foods") are associated with children's weight status. Methods: We use the Classification of Laws Associated with School…

Motor Ability and Weight Status Are Determinants of Out-of-School Activity Participation for Children with Developmental Coordination Disorder

ERIC Educational Resources Information Center

Fong, Shirley S. M.; Lee, Velma Y. L.; Chan, Nerita N. C.; Chan, Rachel S. H.; Chak, Wai-Kwong; Pang, Marco Y. C.

2011-01-01

According to the International Classification of Functioning, Disability and Health model endorsed by the World Health Organization, participation in everyday activities is integral to normal child development. However, little is known about the influence of motor ability and weight status on physical activity participation in children with…

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…

7 CFR 51.2284 - Size classification.

Code of Federal Regulations, 2010 CFR

2010-01-01

...: “Halves”, “Pieces and Halves”, “Pieces” or “Small Pieces”. The size of portions of kernels in the lot... consists of 85 percent or more, by weight, half kernels, and the remainder three-fourths half kernels. (See § 51.2285.) (b) Pieces and halves. Lot consists of 20 percent or more, by weight, half kernels, and the...

75 FR 80430 - Passenger Car and Light Truck Average Fuel Economy Standards Request for Product Plan Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-22

.... Battery Type--classification such as NiMH = Nickel Metal Hydride; Li-ion = Lithium Ion; Li-Air = Lithium...; defined per 49 CFR 523.2. 20. Curb Weight--total weight of vehicle including batteries, lubricants, and... other fuels (or chemical battery energy). 39. Electrical System Voltage--measured in volts, e.g., 12...

Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

PubMed

Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

2018-02-01

In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Anthropometric evaluation of pediatric patients with nonprogressive chronic encephalopathy according to different methods of classification☆

PubMed Central

Teixeira, Jéssica Socas; Gomes, Mirian Martins

2014-01-01

Objective: To perform anthropometric assessment of patients with quadriplegic, chronic non-progressive encephalopathy, comparing two distinct references of nutritional classification and to compare the estimated height to the length measured by stadiometer. Method: Cross-sectional study including 0-3-year children with quadriplegic chronic non-progressive encephalopathy in secondary public hospital. Length, weight, arm circumference, triceps skinfold and knee height were measured. The arm muscle circumference and estimated height were calculated. The following relations were evaluated: weight-for-age, length-for-age and weight-for-length, using as reference the charts of the World Health Organization (WHO) and those proposed by Krick et al. Results: Fourteen children with a mean age of 21 months were evaluated. Assessment of anthropometric indicators showed significant difference between the two classification methods to assess nutritional indicators length/age (p=0.014), weight/age (p=0.014) and weight/length (p=0.001). There was significant correlation between measured length and estimated height (r=0.796, p=0.001). Evaluation of arm circumference and triceps skinfold showed that most patients presented some degree of malnutrition. According to arm muscle circumference, most were eutrophic. Conclusions: Specific curves for children with chronic non-progressive encephalopathy appear to underestimate malnutrition when one takes into account indicators involving weight. Curves developed for healthy children can be a good option for clinical practice and weight-for-length indicator and body composition measurements should be considered as complementary tools. PMID:25479849

Physical fitness of secondary school adolescents in relation to the body weight and the body composition: classification according to Bioelectrical Impedance Analysis. Part II.

PubMed

Chwałczyńska, Agnieszka; Jędrzejewski, Grzegorz; Lewandowski, Zdzisław; Jonak, Wiesława; Sobiech, Krzysztof A

2017-03-01

Underweight and obesity are important factors affecting the level of physical fitness. The aim of this study was to assess physical fitness of lower secondary school adolescents in relation to BMI. Two-hundred students, aged 14-16, were examined. Respondents were divided into 4 groups according to BMI classification. The body height and weight were determined. Physical fitness was assessed on the basis Zuchora's ISF tests. The body weight deficiency occurred in 3% of girls and 5% of boys, overweight was noted in 14% of both groups, and obesity in 6% and 12% accordingly. Statistically significant differences were determined in the components of physical fitness. They were noted in both genders between the group of children with standard body weight and overweight as well as obese children. Significant negative correlations were determined between and the components of physical fitness. More significant correlations giving evidence to the decrease of Zuchora's ISF score along with the increase of BMI were more significant in girls. Statistically significant differences between the boys and girls were determined in all five Zuchora's tests. The highest scores in physical fitness were achieved by the boys and girls with weight deficiency.

Physical fitness of secondary school adolescents in relation to the body weight and the body composition: classification according to World Health Organization. Part I.

PubMed

Chwałczyńska, Agnieszka; Jędrzejewski, Grzegorz; Socha, Małgorzata; Jonak, Wiesława; Sobiech, Krzysztof A

2017-03-01

Underweight and obesity are important factors affecting the level of physical fitness. The aim of this study was to assess physical fitness of lower secondary school adolescents in relation to BMI. Two-hundred students, aged 14-16, were examined. Respondents were divided into 4 groups according to BMI classification. The body height and weight were determined. Physical fitness was assessed on the basis Zuchora's ISF tests. The body weight deficiency occurred in 3% of girls and 5% of boys, overweight was noted in 14% of both groups, and obesity in 6% and 12% accordingly. Statistically significant differences were determined in the components of physical fitness. They were noted in both genders between the group of children with standard body weight and overweight as well as obese children. Significant negative correlations were determined between and the components of physical fitness. More significant correlations giving evidence to the decrease of Zuchora's ISF score along with the increase of BMI were more significant in girls. Statistically significant differences between the boys and girls were determined in all five Zuchora's tests. The highest scores in physical fitness were achieved by the boys and girls with weight deficiency.

[Intellectual and motor development of extremely low birth weight (≤1000 g) children in the 7th year of life; a multicenter, cross-sectional study of children born in the Malopolska voivodship between 2002 and 2004].

PubMed

Kwinta, Przemko; Klimek, Małgorzata; Grudzień, Andrzej; Nitecka, Magdalena; Profus, Krzysztof; Gasińska, Monika; Pawlik, Dorota; Lauterbach, Ryszard; Olechowski, Wiesław; Pietrzyk, Jacek Józef

2012-01-01

A better understanding of the developmental problems in extremely low birth weight (ELBW) preterm infants may enhance their chances of proper adaptation to their environment and make it possible to retrospectively assess perinatal and neonatal methods of treatment. The aim of the study was to evaluate the cognitive and motor development of ELBW children born from 2002 to 2004 in the 7th year of life. Based on these results and perinatal mortality data, it was established what chance the children have to live free of severe complications. Two hundred and four alive newborns with birth weight .1000 g were born in the Malopolska voivodship between 1.09.2002 and 31.08.2004. One hundred and fifteen children (56%) died in early infancy. The study included 81 (91%) children out of the 89 surviving ones. Their mean gestational age at birth was 27.3 weeks. (SD: 2.1 weeks) and their mean birth weight was 840g (SD: 130g). Neurosensory disturbances were assessed in all the children and their cognitive development was evaluated with the use of the WISC-R (Wechsler Intelligence Scale for Children . Revised) scale. The children were divided into 3 groups: group I . normal development (full motor capacity and IQ >84 points and no vision or hearing impairment), group II . mild or moderate impairment (cerebral palsy level I, II or III according to the Gross Motor Function Classification System [GMCS], or IQ 40-84 points, or abnormal vision or hearing, or signs of the hyperactivity syndrome), group III . severe impairment (cerebral palsy level IV, and/or IQ <40 points, or deafness/blindness). Forty-five (56%) children were included in group I, 25 (30%) in group II and 11 (14%) in group III. Moreover, other neurologic abnormalities, such as uneven development, problems with concentration, or abnormal grapho-motor ability were highly prevalent in the group of ELBW children. The incidence of cerebral palsy in the population studied was 16%, the incidence of deafness and severe hearing impairment was 11%, and blindness and severe vision impairment . 12%. In general, the chance of survival free of severe complications was merely 15% in children with birthweight .700 g, 28% in children with birth weight 701- 800 g, 45% in children with birth weight 801-900 g, and 62% in children with birth weight 901-1000 g. 1. The data gathered in a regional study may yield valuable information useful in assessing the prognosis of the general health status of ELBW newborns. 2. Most of the children present uneven development, problems with concentration, or abnormal grapho-motor ability, which may be a cause of learning problems and abnormal relationships with peers. 3. A follow-up study up to adulthood is required for this group of ELBW newborns.

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.

A new classification scheme of plastic wastes based upon recycling labels.

PubMed

Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

2015-01-01

Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fire severity classification: Uses and abuses

Treesearch

Theresa B. Jain; Russell T. Graham

2003-01-01

Burn severity (also referred to as fire severity) is not a single definition, but rather a concept and its classification is a function of the measured units unique to the system of interest. The systems include: flora and fauna, soil microbiology and hydrologic processes, atmospheric inputs, fire management, and society. Depending on the particular system of interest...

Predicting fire severity using surface fuels and moisture

Treesearch

Pamela G. Sikkink; Robert E. Keane

2012-01-01

Fire severity classifications have been used extensively in fire management over the last 30 years to describe specific environmental or ecological impacts of fire on fuels, vegetation, wildlife, and soils in recently burned areas. New fire severity classifications need to be more objective, predictive, and ultimately more useful to fire management and planning. Our...

Definitions for warning signs and signs of severe dengue according to the WHO 2009 classification: Systematic review of literature.

PubMed

Morra, Mostafa Ebraheem; Altibi, Ahmed M A; Iqtadar, Somia; Minh, Le Huu Nhat; Elawady, Sameh Samir; Hallab, Asma; Elshafay, Abdelrahman; Omer, Omer Abedlbagi; Iraqi, Ahmed; Adhikari, Purushottam; Labib, Jonair Hussein; Elhusseiny, Khaled Mosaad; Elgebaly, Ahmed; Yacoub, Sophie; Huong, Le Thi Minh; Hirayama, Kenji; Huy, Nguyen Tien

2018-04-24

Since warning signs and signs of severe dengue are defined differently between studies, we conducted a systematic review on how researchers defined these signs. We conducted an electronic search in Scopus to identify relevant articles, using key words including dengue, "warning signs," "severe dengue," and "classification." A total of 491 articles were identified through this search strategy and were subsequently screened by 2 independent reviewers for definitions of any of the warning or severe signs in the 2009 WHO dengue classification. We included all original articles published in English after 2009, classifying dengue by the 2009 WHO classification or providing the additional definition or criterion of warning signs and severity (besides the information of 2009 WHO). Analysis of the extracted data from 44 articles showed wide variations among definitions and cutoff values used by physicians to classify patients diagnosed with dengue infection. The establishment of clear definitions for warning signs and severity is essential to prevent unnecessary hospitalization and harmonizing the interpretation and comparability of epidemiological studies dedicated to dengue infection. Copyright © 2018 John Wiley & Sons, Ltd.

Pattern classification by memristive crossbar circuits using ex situ and in situ training.

PubMed

Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B

2013-01-01

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

Pattern classification by memristive crossbar circuits using ex situ and in situ training

NASA Astrophysics Data System (ADS)

Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B.

2013-06-01

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

#nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs.

PubMed

Schedl, Markus

2012-01-01

Different term weighting techniques such as [Formula: see text] or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures , normalization techniques , query schemes , index term sets , and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts . We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com. For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb. We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.

Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

NASA Astrophysics Data System (ADS)

Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

2015-01-01

This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

PubMed Central

Ahmed, Moiz; Mehmood, Nadeem; Mehmood, Amir; Rizwan, Kashif

2017-01-01

Objectives Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. Methods We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. Results To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. Conclusions In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios. PMID:28875049

Predicting heart failure mortality in frail seniors: comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0.

PubMed

Tjam, Erin Y; Heckman, George A; Smith, Stuart; Arai, Bruce; Hirdes, John; Poss, Jeff; McKelvie, Robert S

2012-02-23

Though the NYHA functional classification is recommended in clinical settings, concerns have been raised about its reliability particularly among older patients. The RAI 2.0 is a comprehensive assessment system specifically developed for frail seniors. We hypothesized that a prognostic model for heart failure (HF) developed from the RAI 2.0 would be superior to the NYHA classification. The purpose of this study was to determine whether a HF-specific prognostic model based on the RAI 2.0 is superior to the NYHA functional classification in predicting mortality in frail older HF patients. Secondary analysis of data from a prospective cohort study of a HF education program for care providers in long-term care and retirement homes. Univariate analyses identified RAI 2.0 variables predicting death at 6 months. These and the NYHA classification were used to develop logistic models. Two RAI 2.0 models were derived. The first includes six items: "weight gain of 5% or more of total body weight over 30 days", "leaving 25% or more food uneaten", "unable to lie flat", "unstable cognitive, ADL, moods, or behavioural patterns", "change in cognitive function" and "needing help to walk in room"; the C statistic was 0.866. The second includes the CHESS health instability scale and the item "requiring help walking in room"; the C statistic was 0.838. The C statistic for the NYHA scale was 0.686. These results suggest that data from the RAI 2.0, an instrument for comprehensive assessment of frail seniors, can better predict mortality than the NYHA classification. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups

PubMed Central

2016-01-01

Background More than half of all smartphone app downloads involve weight, diet, and exercise. If successful, these lifestyle apps may have far-reaching effects for disease prevention and health cost-savings, but few researchers have analyzed data from these apps. Objective The purposes of this study were to analyze data from a commercial health app (Lose It!) in order to identify successful weight loss subgroups via exploratory analyses and to verify the stability of the results. Methods Cross-sectional, de-identified data from Lose It! were analyzed. This dataset (n=12,427,196) was randomly split into 24 subsamples, and this study used 3 subsamples (combined n=972,687). Classification and regression tree methods were used to explore groupings of weight loss with one subsample, with descriptive analyses to examine other group characteristics. Data mining validation methods were conducted with 2 additional subsamples. Results In subsample 1, 14.96% of users lost 5% or more of their starting body weight. Classification and regression tree analysis identified 3 distinct subgroups: “the occasional users” had the lowest proportion (4.87%) of individuals who successfully lost weight; “the basic users” had 37.61% weight loss success; and “the power users” achieved the highest percentage of weight loss success at 72.70%. Behavioral factors delineated the subgroups, though app-related behavioral characteristics further distinguished them. Results were replicated in further analyses with separate subsamples. Conclusions This study demonstrates that distinct subgroups can be identified in “messy” commercial app data and the identified subgroups can be replicated in independent samples. Behavioral factors and use of custom app features characterized the subgroups. Targeting and tailoring information to particular subgroups could enhance weight loss success. Future studies should replicate data mining analyses to increase methodology rigor. PMID:27301853

A quantitative weight of evidence methodology for the assessment of reproductive and developmental toxicity and its application for classification and labeling of chemicals.

PubMed

Dekant, Wolfgang; Bridges, James

2016-12-01

Hazard assessment of chemicals usually applies narrative assessments with a number of weaknesses. Therefore, application of weight of evidence (WoE) approaches are often mandated but guidance to perform a WoE assessment is lacking. This manuscript describes a quantitative WoE (QWoE) assessment for reproductive toxicity data and its application for classification and labeling (C&L). Because C&L criteria are based on animal studies, the scope is restricted to animal toxicity data. The QWoE methodology utilizes numerical scoring sheets to assess reliability of a publication and the toxicological relevance of reported effects. Scores are given for fourteen quality aspects, best practice receives the highest score. The relevance/effects scores (0 to four) are adjusted to the key elements of the toxic response for the endpoint and include weighting factors for effects on different levels of the biological organization. The relevance/effects scores are then assessed against the criteria dose-response, magnitude and persistence of effects, consistency of observations with the hypothesis, and relation of effects to human disease. The quality/reliability scores and the relevance/effect scores are then multiplied to give a numerical strength of evidence for adverse effects. This total score is then used to assign the chemical to the different classes employed in classification. Copyright © 2016 Elsevier Inc. All rights reserved.

Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression.

PubMed

Li, Juntao; Wang, Yanyan; Jiang, Tao; Xiao, Huimin; Song, Xuekun

2018-05-09

Diagnosing acute leukemia is the necessary prerequisite to treating it. Multi-classification on the gene expression data of acute leukemia is help for diagnosing it which contains B-cell acute lymphoblastic leukemia (BALL), T-cell acute lymphoblastic leukemia (TALL) and acute myeloid leukemia (AML). However, selecting cancer-causing genes is a challenging problem in performing multi-classification. In this paper, weighted gene co-expression networks are employed to divide the genes into groups. Based on the dividing groups, a new regularized multinomial regression with overlapping group lasso penalty (MROGL) has been presented to simultaneously perform multi-classification and select gene groups. By implementing this method on three-class acute leukemia data, the grouped genes which work synergistically are identified, and the overlapped genes shared by different groups are also highlighted. Moreover, MROGL outperforms other five methods on multi-classification accuracy. Copyright © 2017. Published by Elsevier B.V.

Nutritional status in sick children and adolescents is not accurately reflected by BMI-SDS.

PubMed

Fusch, Gerhard; Raja, Preeya; Dung, Nguyen Quang; Karaolis-Danckert, Nadina; Barr, Ronald; Fusch, Christoph

2013-01-01

Nutritional status provides helpful information of disease severity and treatment effectiveness. Body mass index standard deviation scores (BMI-SDS) provide an approximation of body composition and thus are frequently used to classify nutritional status of sick children and adolescents. However, the accuracy of estimating body composition in this population using BMI-SDS has not been assessed. Thus, this study aims to evaluate the accuracy of nutritional status classification in sick infants and adolescents using BMI-SDS, upon comparison to classification using percentage body fat (%BF) reference charts. BMI-SDS was calculated from anthropometric measurements and %BF was measured using dual-energy x-ray absorptiometry (DXA) for 393 sick children and adolescents (5 months-18 years). Subjects were classified by nutritional status (underweight, normal weight, overweight, and obese), using 2 methods: (1) BMI-SDS, based on age- and gender-specific percentiles, and (2) %BF reference charts (standard). Linear regression and a correlation analysis were conducted to compare agreement between both methods of nutritional status classification. %BF reference value comparisons were also made between 3 independent sources based on German, Canadian, and American study populations. Correlation between nutritional status classification by BMI-SDS and %BF agreed moderately (r (2) = 0.75, 0.76 in boys and girls, respectively). The misclassification of nutritional status in sick children and adolescents using BMI-SDS was 27% when using German %BF references. Similar rates observed when using Canadian and American %BF references (24% and 23%, respectively). Using BMI-SDS to determine nutritional status in a sick population is not considered an appropriate clinical tool for identifying individual underweight or overweight children or adolescents. However, BMI-SDS may be appropriate for longitudinal measurements or for screening purposes in large field studies. When accurate nutritional status classification of a sick patient is needed for clinical purposes, nutritional status will be assessed more accurately using methods that accurately measure %BF, such as DXA.

Effects of intrauterine retention and postmortem interval on body weight following intrauterine death: implications for assessment of fetal growth restriction at autopsy.

PubMed

Man, J; Hutchinson, J C; Ashworth, M; Heazell, A E; Levine, S; Sebire, N J

2016-11-01

According to the classification system used, 15-60% of stillbirths remain unexplained, despite undergoing recommended autopsy examination, with variable attribution of fetal growth restriction (FGR) as a cause of death. Distinguishing small-for-gestational age (SGA) from pathological FGR is a challenge at postmortem examination. This study uses data from a large, well-characterized series of intrauterine death autopsies to investigate the effects of secondary changes such as fetal maceration, intrauterine retention and postmortem interval on body weight. Autopsy findings from intrauterine death investigations (2005-2013 inclusive, from Great Ormond Street Hospital and St George's Hospital, London) were collated into a research database. Growth charts published by the World Health Organization were used to determine normal expected weight centiles for fetuses born ≥ 24 weeks' gestation, and the effects of intrauterine retention (maceration) and postmortem interval were calculated. There were 1064 intrauterine deaths, including 533 stillbirths ≥ 24 weeks' gestation with a recorded birth weight. Of these, 192 (36%) had an unadjusted birth weight below the 10 th centile and were defined as SGA. The majority (86%) of stillborn SGA fetuses demonstrated some degree of maceration, indicating a significant period of intrauterine retention after death. A significantly greater proportion of macerated fetuses were present in the SGA population compared with the non-SGA population (P = 0.01). There was a significant relationship between increasing intrauterine retention interval and both more severe maceration and reduction in birth weight (P < 0.0001 for both), with an average artifactual reduction in birth weight of around -0.8 SD of expected weight. There was an average 12% reduction in fetal weight between delivery and autopsy and, as postmortem interval increased, fetal weight loss increased (P = 0.0001). Based on birth weight alone, 36% of stillbirths are classified as SGA. However, fetuses lose weight in utero with increasing intrauterine retention and continue to lose weight between delivery and autopsy, resulting in erroneous overestimation of FGR. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks.

PubMed

Ying, Jun; Dutta, Joyita; Guo, Ning; Hu, Chenhui; Zhou, Dan; Sitek, Arkadiusz; Li, Quanzheng

2016-12-21

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Webcam classification using simple features

NASA Astrophysics Data System (ADS)

Pramoun, Thitiporn; Choe, Jeehyun; Li, He; Chen, Qingshuang; Amornraksa, Thumrongrat; Lu, Yung-Hsiang; Delp, Edward J.

2015-03-01

Thousands of sensors are connected to the Internet and many of these sensors are cameras. The "Internet of Things" will contain many "things" that are image sensors. This vast network of distributed cameras (i.e. web cams) will continue to exponentially grow. In this paper we examine simple methods to classify an image from a web cam as "indoor/outdoor" and having "people/no people" based on simple features. We use four types of image features to classify an image as indoor/outdoor: color, edge, line, and text. To classify an image as having people/no people we use HOG and texture features. The features are weighted based on their significance and combined. A support vector machine is used for classification. Our system with feature weighting and feature combination yields 95.5% accuracy.

Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.

PubMed

Hayes, Timothy; Usami, Satoshi; Jacobucci, Ross; McArdle, John J

2015-12-01

In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. (c) 2015 APA, all rights reserved).

Using Classification and Regression Trees (CART) and Random Forests to Analyze Attrition: Results From Two Simulations

PubMed Central

Hayes, Timothy; Usami, Satoshi; Jacobucci, Ross; McArdle, John J.

2016-01-01

In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers. PMID:26389526

[Evaluation of the course of chronic obstructive lung diseases according to the classifications of the European Respiratory Society and the Global Initiative on Chronic Obstructive Lung Disease].

PubMed

Nefedov, V B; Shergina, E A; Popova, L A

2006-01-01

In 91 patients with chronic obstructive lung disease (COLD), the severity of this disease according to the Classifications of the European Respiratory Society (ERS) and the Global Initiative on Chronic Obstructive Lung Disease (GOLD) was compared with that of pulmonary dysfunction according to the data of a comprehensive study, involving the determination of bronchial patency, lung volumes, capacities, and gas-exchange function. This follows that the ERS and GOLD classifications are to be positively appraised as they provide an eligible group of patients for clinical practice in terms of the severity of pulmonary dysfunction and that of COLD. However, the concomitant clinical use of both classifications cannot be regarded as justifiable due to that there are differences in the number of detectable grades (stages) of COLD and borderline (COLD differentiating grades (stages) values of EFV1). In this connection, both classifications have approximately equally significant merits and shortcomings and it is practically impossible to give preference to one of them as the best one. The optimal way out of the established situation is to develop a new (improved) classification of the severity of COLD on the bases of these two existing classifications.

Improving the accuracy of k-nearest neighbor using local mean based and distance weight

NASA Astrophysics Data System (ADS)

Syaliman, K. U.; Nababan, E. B.; Sitompul, O. S.

2018-03-01

In k-nearest neighbor (kNN), the determination of classes for new data is normally performed by a simple majority vote system, which may ignore the similarities among data, as well as allowing the occurrence of a double majority class that can lead to misclassification. In this research, we propose an approach to resolve the majority vote issues by calculating the distance weight using a combination of local mean based k-nearest neighbor (LMKNN) and distance weight k-nearest neighbor (DWKNN). The accuracy of results is compared to the accuracy acquired from the original k-NN method using several datasets from the UCI Machine Learning repository, Kaggle and Keel, such as ionosphare, iris, voice genre, lower back pain, and thyroid. In addition, the proposed method is also tested using real data from a public senior high school in city of Tualang, Indonesia. Results shows that the combination of LMKNN and DWKNN was able to increase the classification accuracy of kNN, whereby the average accuracy on test data is 2.45% with the highest increase in accuracy of 3.71% occurring on the lower back pain symptoms dataset. For the real data, the increase in accuracy is obtained as high as 5.16%.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Non-Hodgkin lymphoma

MedlinePlus

... There are many types of NHL. One classification (grouping) is by how fast the cancer spreads. The ... The stage when you are first diagnosed Your age and overall health Symptoms, including weight loss, fever, ...

Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters

NASA Astrophysics Data System (ADS)

Nasir, Ahmad Fakhri Ab; Suhaila Sabarudin, Siti; Majeed, Anwar P. P. Abdul; Ghani, Ahmad Shahrizan Abdul

2018-04-01

Chicken egg is a source of food of high demand by humans. Human operators cannot work perfectly and continuously when conducting egg grading. Instead of an egg grading system using weight measure, an automatic system for egg grading using computer vision (using egg shape parameter) can be used to improve the productivity of egg grading. However, early hypothesis has indicated that more number of egg classes will change when using egg shape parameter compared with using weight measure. This paper presents the comparison of egg classification by the two above-mentioned methods. Firstly, 120 images of chicken eggs of various grades (A–D) produced in Malaysia are captured. Then, the egg images are processed using image pre-processing techniques, such as image cropping, smoothing and segmentation. Thereafter, eight egg shape features, including area, major axis length, minor axis length, volume, diameter and perimeter, are extracted. Lastly, feature selection (information gain ratio) and feature extraction (principal component analysis) are performed using k-nearest neighbour classifier in the classification process. Two methods, namely, supervised learning (using weight measure as graded by egg supplier) and unsupervised learning (using egg shape parameters as graded by ourselves), are conducted to execute the experiment. Clustering results reveal many changes in egg classes after performing shape-based grading. On average, the best recognition results using shape-based grading label is 94.16% while using weight-based label is 44.17%. As conclusion, automated egg grading system using computer vision is better by implementing shape-based features since it uses image meanwhile the weight parameter is more suitable by using weight grading system.

Relationship between the surrogate anthropometric measures, foot length and chest circumference and birth weight among newborns of Sarlahi, Nepal.

PubMed

Mullany, L C; Darmstadt, G L; Khatry, S K; Leclerq, S C; Tielsch, J M

2007-01-01

Classification of infants into low birth weight (LBW, <2500 g) or very low birth weight (VLBW, <2000 g) categories is a crucial step in targeting interventions to high-risk infants. To compare the validity of chest circumference and foot length as surrogate anthropometric measures for the identification of LBW and VLBW infants. Newborn infants (n=1640) born between March and June 2004 in 30 Village Development Committees of Sarlahi district, Nepal. Chest circumference, foot length and weight (SECA 727, precise to 2 g) of newborns were measured within 72 h after birth. The sensitivity, specificity and predictive values for a range of cutoff points of the anthropometric measures were estimated using the digital scale measurements as the gold standard. Among LBW infants (469/1640, 28.6%), chest circumference measures <30.3 cm were 91% sensitive and 83% specific. Similar levels of sensitivity for foot length were achieved only with considerable loss of specificity (<45%). Foot length measurements <6.9 cm were 88% sensitive and 86% specific for the identification of VLBW infants. Chest circumference was superior to foot length in classification of infants into birth weight categories. For the identification of VLBW infants, foot length performed well, and may be preferable to chest circumference, as the former measure does not require removal of infant swaddling clothes. In the absence of more precise direct measures of birth weight, chest circumference is recommended over foot length for the identification of LBW infants.

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

PubMed

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

PubMed Central

Roh, Jongryun; Park, Hyeong-jun; Lee, Kwang Jin; Hyeong, Joonho; Kim, Sayup

2018-01-01

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. PMID:29329261

A Study on the Correlation of Pertrochanteric Osteoporotic Fracture Severity with the Severity of Osteoporosis.

PubMed

Hayer, Prabhnoor Singh; Deane, Anit Kumar Samuel; Agrawal, Atul; Maheshwari, Rajesh; Juyal, Anil

2016-04-01

Osteoporosis is a metabolic bone disease caused by progressive bone loss. It is characterized by low Bone Mineral Density (BMD) and structural deterioration of bone tissue leading to bone fragility and increased risk of fractures. When classifying a fracture, high reliability and validity are crucial for successful treatment. Furthermore, a classification system should include severity, method of treatment, and prognosis for any given fracture. Since it is known that treatment significantly influences prognosis, a classification system claiming to include both would be desirable. Since there is no such classification system, which includes both the fracture type and the osteoporosis severity, we tried to find a correlation between fracture severity and osteoporosis severity. The aim of the study was to evaluate whether the AO/ASIF fracture classification system, which indicates the severity of fractures, has any relationship with the bone mineral status in patients with primary osteoporosis. We hypothesized that fracture severity and severity of osteoporosis should show some correlation. An observational analytical study was conducted over a period of one year during which 49 patients were included in the study at HIMS, SRH University, Dehradun. The osteoporosis status of all the included patients with a pertrochanteric fracture was documented using a DEXA scan and T-Score (BMD) was calculated. All patients had a trivial trauma. All the fractures were classified as per AO/ASIF classification. Pearson Correlation between BMD and fracture type was calculated. Data was entered on Microsoft Office Excel version 2007 and Interpretation and analysis of obtained data was done using summary statistics. Pearson Correlation between BMD and fracture type was calculated using the SPSS software version 22.0. The average age of the patients included in the study was 71.2 years and the average bone mineral density was -4.9. The correlation between BMD and fracture type was calculated and the r-values obtained was 0.180, which showed low a correlation and p-value was 0.215, which was insignificant. Statistically the pertrochanteric fracture configuration as per AO Classification does not correlate with the osteoporosis severity of the patient.

Experiment Design for Nonparametric Models Based On Minimizing Bayes Risk: Application to Voriconazole1

PubMed Central

Bayard, David S.; Neely, Michael

2016-01-01

An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a nonparametric model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher Information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the nonparametric model. Specifically, the problem of identifying an individual from a nonparametric prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient’s behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (Multiple-Model Optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications. PMID:27909942

Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole¹.

PubMed

Bayard, David S; Neely, Michael

2017-04-01

An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a NP model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the NP model. Specifically, the problem of identifying an individual from a NP prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient's behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (multiple-model optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

77 FR 44456 - Classification of Two Steroids, Prostanozol

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-30

... by positive nitrogen balance and protein metabolism, resulting in increases in protein synthesis and... activity by means of nitrogen balance and androgenic activity based on weight changes of the ventral...

Wake Turbulence

DOT National Transportation Integrated Search

1997-07-06

THIS IS A SAFETY NOTICE. The guidance contained herein supersedes : the guidance provided in the current edition of Order 7110.65, Air Traffic Control, relating to selected wake turbulence separations and aircraft weight classifications. This Notice ...

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Classification of Complex Sounds.

DTIC Science & Technology

1992-10-31

spectral weights may be useful in developing signal enhancement techniques based on psychological aspects of the listener (providing a complement to...Journals) Green, D.M., and Berg, B.G. (1991). Spectral weights and the profile bowl. Quarterly Journal of Experimental Psychology , 43A, 449-458. Dai, H...Macmillan and C.D. Creelman . Cambridge/NY: Cambridge Universi- ty Press, 1991.) J. Math. Psych., in press. Training Currently, there are two graduate

[Fibrocystic disease of the pancreas: a presentation feature. Anatomopathological report].

PubMed

Heffes Nahmod, L A; Ortiz, J; Cervetto, J L; Guastavino, E; Boffi, A

1984-01-01

Five patients with CF (cystic fibrosis) dead between 1974 to 1982 at ages ranging from one to six months are presented. All of them showed edema, hypoalbuminemia and anemia in a severely compromised clinical situation, and failure to gain weight in spite of being breast-fed in the first weeks of life, in four of them. All of them were second or third degree malnourished babies (Gomez classification) at admission. Five children presented edema, two severe, two moderate and one mild. Hematocrit values ranged from 19% to 39% (means 26.4%), and albuminemia from 1.60 to 3.00 g/% (means 2.14 g/%). Two patients presented antecedents of dead brothers. All of them received substitution therapy with pancreatic enzymes. The children dead within seven and seventeen days of admission (means ten days) of broncho-pulmonar disfunction. In this work, we wish to call the pediatrician's attention about the importance of making this diagnostic presumption in the first months of the life.

Botulinum toxin type A in children and adolescents with severe cerebral palsy: a retrospective chart review.

PubMed

Mesterman, Ronit; Gorter, Jan Willem; Harvey, Adrienne; Lockhart, Julia; McEwen-Hill, Jenny; Margallo, Karen; Goldie, Nancy

2014-02-01

This retrospective cohort study reviewed set goals and their outcomes of children and adolescents with severe cerebral palsy who received botulinum toxin A in 2008 and 2009. Sixty children (36 male, mean age 9 years) were included. They received on average 4 (range 1-7) treatments, with the dosage varying between 20 and 400 units per treatment (3-21 U/kg/body weight). Mild transient side effects were reported in 12 of 242 treatments with botulinum toxin A. Treatment goals were related to lower limb function (82%), range of motion (68%), positioning (33%), upper limb function (33%), and facilitating ease of care in dressing (30%), toileting, and diapering (22%). The treatment goals were reached in 60% to 85% by report of the parent and child dyad. Our findings suggest that botulinum toxin A should be considered as a treatment option in patients with cerebral palsy within Gross Motor Function Classification System levels IV and V.

Predictive factors of difficulty in lower third molar extraction: A prospective cohort study.

PubMed

Alvira-González, J; Figueiredo, R; Valmaseda-Castellón, E; Quesada-Gómez, C; Gay-Escoda, C

2017-01-01

Several publications have measured the difficulty of third molar removal, trying to establish the main risk factors, however several important preoperative and intraoperative variables are overlooked. A prospective cohort study comprising a total of 130 consecutive lower third molar extractions was performed. The outcome variables used to measure the difficulty of the extraction were operation time and a 100mm visual analogue scale filled by the surgeon at the end of the surgical procedure. The predictors were divided into 4 different groups (demographic, anatomic, radiographic and operative variables). A descriptive, bivariate and multivariate analysis of the data was performed. Patients' weight, the presence of bulbous roots, the need to perform crown and root sectioning of the lower third molar and Pell and Gregory 123 classification significantly influenced both outcome variables (p< 0.05). Certain anatomical, radiological and operative variables appear to be important factors in the assessment of surgical difficulty in the extraction of lower third molars.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

PubMed

Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

2016-01-01

Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

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.

A new ambulatory classification and funding model for radiation oncology: non-admitted patients in Victorian hospitals.

PubMed

Antioch, K M; Walsh, M K; Anderson, D; Wilson, R; Chambers, C; Willmer, P

1998-01-01

The Victorian Department of Human Services has developed a classification and funding model for non-admitted radiation oncology patients. Agencies were previously funded on an historical cost input basis. For 1996-97, payments were made according to the new Non-admitted Radiation Oncology Classification System and include four key components. Fixed grants are based on Weighted Radiation Therapy Services targets for megavoltage courses, planning procedures (dosimetry and simulation) and consultations. The additional throughput pool covers additional Weighted Radiation Therapy Services once targets are reached, with access conditional on the utilisation of a minimum number of megavoltage fields by each hospital. Block grants cover specialised treatments, such as brachytherapy, allied health payments and other support services. Compensation grants were available to bring payments up to the level of the previous year. There is potential to provide incentives to promote best practice in Australia through linking appropriate practice to funding models. Key Australian and international developments should be monitored, including economic evaluation studies, classification and funding models, and the deliberations of the American College of Radiology, the American Society for Therapeutic Radiology and Oncology, the Trans-Tasman Radiation Oncology Group and the Council of Oncology Societies of Australia. National impact on clinical practice guidelines in Australia can be achieved through the Quality of Care and Health Outcomes Committee of the National Health and Medical Research Council.

Noncontrast Magnetic Resonance Lymphography.

PubMed

Arrivé, Lionel; Derhy, Sarah; El Mouhadi, Sanaâ; Monnier-Cholley, Laurence; Menu, Yves; Becker, Corinne

2016-01-01

Different imaging techniques have been used for the investigation of the lymphatic channels and lymph glands. Noncontrast magnetic resonance (MR) lymphography has significant advantages in comparison with other imaging modalities. Noncontrast MR lymphography uses very heavily T2-weighted fast spin echo sequences which obtain a nearly complete signal loss in tissue background and specific display of lymphatic vessels with a long T2 relaxation time. The raw data can be processed with different algorithms such as maximum intensity projection algorithm to obtain an anatomic representation. Standard T2-weighted MR images easily demonstrate the location of edema. It appears as subcutaneous infiltration of soft tissue with a classical honeycomb pattern. True collection around the muscular area may be demonstrated in case of severe lymphedema. Lymph nodes may be normal in size, number, and signal intensity; in other cases, lymph nodes may be smaller in size or number of lymph nodes may be restricted. MR lymphography allows a classification of lymphedema in aplasia (no collecting vessels demonstrated); hypoplasia (a small number of lymphatic vessels), and numerical hyperplasia or hyperplasia (with an increased number of lymphatic vessels of greater and abnormal diameter). Noncontrast MR lymphography is a unique noninvasive imaging modality for the diagnosis of lymphedema. It can be used for positive diagnosis, differential diagnosis, and specific evaluation of lymphedema severity. It may also be used for follow-up evaluation after treatment. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Pathohistological classification systems in gastric cancer: Diagnostic relevance and prognostic value

PubMed Central

Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan

2014-01-01

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer. PMID:24914328

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

Thomas, Minta; De Brabanter, Kris; Suykens, Johan A K; De Moor, Bart

2014-12-31

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

The relationship of newborn adiposity to fetal growth outcome based on birth weight or the modified neonatal growth assessment score

PubMed Central

Lee, W; Riggs, T; Koo, W; Deter, RL; Yeo, L; Romero, R

2013-01-01

Objectives (1) Develop reference ranges of neonatal adiposity using air displacement plethysmography. (2) Use new reference ranges for neonatal adiposity to compare two different methods of evaluating neonatal nutritional status. Methods Three hundred and twenty-four normal neonates (35–41 weeks post-menstrual age) had body fat (%BF) and total fat mass (FM, g) measured using air displacement plethysmography shortly after delivery. Results were stratified for 92 of these neonates with corresponding fetal biometry using two methods for classifying nutritional status: (1) population-based weight percentiles; and (2) a modified neonatal growth assessment score (m3NGAS51). Results At the 50th percentile, &BF varied from 7.7% (35 weeks) to 11.8% (41 weeks), while the corresponding 50th percentiles for total FM were 186–436g. Among the subset of 92 neonates, no significant differences in adiposity were found between small for gestational age (SGA), appropriate for gestational age (AGA), and large for gestational age (LGA) groups using population-based weight standards. Classification of the same neonates using m3NGAS51 showed significant differences in mean %BF between corresponding groups. Conclusions Population-based weight criteria for neonatal nutritional status can lead to misclassification on the basis of adiposity. A neonatal growth assessment score, that considers the growth potential of several anatomic parameters, appears to more effectively classify under-and over-nourished newborns. PMID:22494346

Tree mortality based fire severity classification for forest inventories: A Pacific Northwest national forests example

Treesearch

Thomas R. Whittier; Andrew N. Gray

2016-01-01

Determining how the frequency, severity, and extent of forest fires are changing in response to changes in management and climate is a key concern in many regions where fire is an important natural disturbance. In the USA the only national-scale fire severity classification uses satellite image changedetection to produce maps for large (>400 ha) fires, and is...

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Guidelines on severity assessment and classification of genetically altered mouse and rat lines.

PubMed

Zintzsch, Anne; Noe, Elena; Reißmann, Monika; Ullmann, Kristina; Krämer, Stephanie; Jerchow, Boris; Kluge, Reinhart; Gösele, Claudia; Nickles, Hannah; Puppe, Astrid; Rülicke, Thomas

2017-12-01

Genetic alterations can unpredictably compromise the wellbeing of animals. Thus, more or less harmful phenotypes might appear in the animals used in research projects even when they are not subjected to experimental treatments. The severity classification of suffering has become an important issue since the implementation of Directive 2010/63/EU on the protection of animals used for scientific purposes. Accordingly, the breeding and maintenance of genetically altered (GA) animals which are likely to develop a harmful phenotype has to be authorized. However, a determination of the degree of severity is rather challenging due to the large variety of phenotypes. Here, the Working Group of Berlin Animal Welfare Officers (WG Berlin AWO) provides field-tested guidelines on severity assessment and classification of GA rodents. With a focus on basic welfare assessment and severity classification we provide a list of symptoms that have been classified as non-harmful, mild, moderate or severe burdens. Corresponding monitoring and refinement strategies as well as specific housing requirements have been compiled and are strongly recommended to improve hitherto applied breeding procedures and conditions. The document serves as a guide to determine the degree of severity for an observed phenotype. The aim is to support scientists, animal care takers, animal welfare bodies and competent authorities with this task, and thereby make an important contribution to a European harmonization of severity assessments for the continually increasing number of GA rodents.

Phytoplankton global mapping from space with a support vector machine algorithm

NASA Astrophysics Data System (ADS)

de Boissieu, Florian; Menkes, Christophe; Dupouy, Cécile; Rodier, Martin; Bonnet, Sophie; Mangeas, Morgan; Frouin, Robert J.

2014-11-01

In recent years great progress has been made in global mapping of phytoplankton from space. Two main trends have emerged, the recognition of phytoplankton functional types (PFT) based on reflectance normalized to chlorophyll-a concentration, and the recognition of phytoplankton size class (PSC) based on the relationship between cell size and chlorophyll-a concentration. However, PFTs and PSCs are not decorrelated, and one approach can complement the other in a recognition task. In this paper, we explore the recognition of several dominant PFTs by combining reflectance anomalies, chlorophyll-a concentration and other environmental parameters, such as sea surface temperature and wind speed. Remote sensing pixels are labeled thanks to coincident in-situ pigment data from GeP&CO, NOMAD and MAREDAT datasets, covering various oceanographic environments. The recognition is made with a supervised Support Vector Machine classifier trained on the labeled pixels. This algorithm enables a non-linear separation of the classes in the input space and is especially adapted for small training datasets as available here. Moreover, it provides a class probability estimate, allowing one to enhance the robustness of the classification results through the choice of a minimum probability threshold. A greedy feature selection associated to a 10-fold cross-validation procedure is applied to select the most discriminative input features and evaluate the classification performance. The best classifiers are finally applied on daily remote sensing datasets (SeaWIFS, MODISA) and the resulting dominant PFT maps are compared with other studies. Several conclusions are drawn: (1) the feature selection highlights the weight of temperature, chlorophyll-a and wind speed variables in phytoplankton recognition; (2) the classifiers show good results and dominant PFT maps in agreement with phytoplankton distribution knowledge; (3) classification on MODISA data seems to perform better than on SeaWIFS data, (4) the probability threshold screens correctly the areas of smallest confidence such as the interclass regions.

Low bone mineral density in ambulatory persons with cerebral palsy? A systematic review.

PubMed

Mus-Peters, Cindy T R; Huisstede, Bionka M A; Noten, Suzie; Hitters, Minou W M G C; van der Slot, Wilma M A; van den Berg-Emons, Rita J G

2018-05-22

Non-ambulatory persons with cerebral palsy are prone to low bone mineral density. In ambulatory persons with cerebral palsy, bone mineral density deficits are expected to be small or absent, but a consensus conclusion is lacking. In this systematic review bone mineral density in ambulatory persons with cerebral palsy (Gross Motor Function Classification Scales I-III) was studied. Medline, Embase, and Web of Science were searched. According to international guidelines, low bone mineral density was defined as Z-score ≤ -2.0. In addition, we focused on Z-score ≤ -1.0 because this may indicate a tendency towards low bone mineral density. We included 16 studies, comprising 465 patients aged 1-65 years. Moderate and conflicting evidence for low bone mineral density (Z-score ≤ -2.0) was found for several body parts (total proximal femur, total body, distal femur, lumbar spine) in children with Gross Motor Function Classification Scales II and III. We found no evidence for low bone mineral density in children with Gross Motor Function Classification Scale I or adults, although there was a tendency towards low bone mineral density (Z-score ≤ -1.0) for several body parts. Although more high-quality research is needed, results indicate that deficits in bone mineral density are not restricted to non-ambulatory people with cerebral palsy. Implications for Rehabilitation Although more high-quality research is needed, including adults and fracture risk assessment, the current study indicates that deficits in bone mineral density are not restricted to non-ambulatory people with CP. Health care professionals should be aware that optimal nutrition, supplements on indication, and an active lifestyle, preferably with weight-bearing activities, are important in ambulatory people with CP, also from a bone quality point-of-view. If indicated, medication and fall prevention training should be prescribed.

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

Radiographic classifications in Perthes disease

PubMed Central

Huhnstock, Stefan; Svenningsen, Svein; Merckoll, Else; Catterall, Anthony; Terjesen, Terje; Wiig, Ola

2017-01-01

Background and purpose Different radiographic classifications have been proposed for prediction of outcome in Perthes disease. We assessed whether the modified lateral pillar classification would provide more reliable interobserver agreement and prognostic value compared with the original lateral pillar classification and the Catterall classification. Patients and methods 42 patients (38 boys) with Perthes disease were included in the interobserver study. Their mean age at diagnosis was 6.5 (3–11) years. 5 observers classified the radiographs in 2 separate sessions according to the Catterall classification, the original and the modified lateral pillar classifications. Interobserver agreement was analysed using weighted kappa statistics. We assessed the associations between the classifications and femoral head sphericity at 5-year follow-up in 37 non-operatively treated patients in a crosstable analysis (Gamma statistics for ordinal variables, γ). Results The original lateral pillar and Catterall classifications showed moderate interobserver agreement (kappa 0.49 and 0.43, respectively) while the modified lateral pillar classification had fair agreement (kappa 0.40). The original lateral pillar classification was strongly associated with the 5-year radiographic outcome, with a mean γ correlation coefficient of 0.75 (95% CI: 0.61–0.95) among the 5 observers. The modified lateral pillar and Catterall classifications showed moderate associations (mean γ correlation coefficient 0.55 [95% CI: 0.38–0.66] and 0.64 [95% CI: 0.57–0.72], respectively). Interpretation The Catterall classification and the original lateral pillar classification had sufficient interobserver agreement and association to late radiographic outcome to be suitable for clinical use. Adding the borderline B/C group did not increase the interobserver agreement or prognostic value of the original lateral pillar classification. PMID:28613966

Annual update of data for estimating ESALs.

DOT National Transportation Integrated Search

2006-10-01

A revised procedure for estimating equivalent single axleloads (ESALs) was developed in 1985. This procedure used weight, classification, and traffic volume data collected by the Transportation Cabinet's Division of Planning. : Annual updates of data...

10 CFR 71.33 - Package description.

Code of Federal Regulations, 2010 CFR

2010-01-01

...) Classification as Type B(U), Type B(M), or fissile material packaging; (2) Gross weight; (3) Model number; (4... absorbers or moderators, and the atomic ratio of moderator to fissile constituents; (5) Maximum normal...

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).

Colorectal Cancer and Colitis Diagnosis Using Fourier Transform Infrared Spectroscopy and an Improved K-Nearest-Neighbour Classifier.

PubMed

Li, Qingbo; Hao, Can; Kang, Xue; Zhang, Jialin; Sun, Xuejun; Wang, Wenbo; Zeng, Haishan

2017-11-27

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

Automatic morphological classification of galaxy images

PubMed Central

Shamir, Lior

2009-01-01

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbor rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with accuracy of ~90% compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects. PMID:20161594

Occupation-specific absenteeism costs associated with obesity and morbid obesity.

PubMed

Cawley, John; Rizzo, John A; Haas, Kara

2007-12-01

To document the absenteeism costs associated with obesity and morbid obesity by occupation. Data from the Medical Expenditure Panel Survey for 2000-2004 are examined. The outcomes are probability of missing any work in the previous year and number of days of work missed in the previous year. Predictors include clinical weight classification, age, education, and race. Models are estimated separately by gender and occupation category. The probability of missing work in the past year, number of days missed, and costs of absenteeism rise with clinical weight classification for both women and men, and vary across occupation. Absenteeism costs associated with obesity total $4.3 billion annually in the United States. Substantial absenteeism costs are associated with obesity and morbid obesity. Employers should explore workplace interventions and health insurance expansions to reduce these costs.

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

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

2008-01-01

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

Moderate Psoriasis: A Proposed Definition.

PubMed

Llamas-Velasco, M; de la Cueva, P; Notario, J; Martínez-Pilar, L; Martorell, A; Moreno-Ramírez, D

2017-12-01

The Psoriasis Area Severity Index (PASI) is the most widely used scale for assessing the severity of psoriasis and for therapeutic decision making. On the basis of the PASI score, patients have been stratified into 2 groups: mild disease and moderate-to-severe disease. To draft a proposal for the definition and characterization of moderate psoriasis based on PASI and Dermatology Life Quality Index (DLQI) scores. A group of 6 dermatologists with experience in the treatment of psoriasis undertook a critical review of the literature and a discussion of cases to draft a proposal. In order of priority, PASI, DLQI, and body surface area (BSA) are the parameters to be used in daily practice to classify psoriasis as mild, moderate, or severe. Severity should be assessed on the basis of a combined evaluation and interpretation of the PASI and DLQI. And 3, PASI and DLQI should carry equal weight in the determination of disease severity. On this basis, psoriasis severity was defined using the following criteria: mild, PASI<7 and DLQI<7; moderate, PASI=7-15 and DLQI=5-15 (classified as severe when difficult-to-treat sites are affected or when there is a significant psychosocial impact); severe, PASI >15, independently of the DLQI score. A more precise classification of psoriasis according to disease severity will improve the risk-benefit assessment essential to therapeutic decision making in these patients. Copyright © 2017 AEDV. Publicado por Elsevier España, S.L.U. All rights reserved.

Classifications for Cesarean Section: A Systematic Review

PubMed Central

Torloni, Maria Regina; Betran, Ana Pilar; Souza, Joao Paulo; Widmer, Mariana; Allen, Tomas; Gulmezoglu, Metin; Merialdi, Mario

2011-01-01

Background Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system. Methods and Findings Three electronic databases were searched for classifications published 1968–2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2–9 (maximum grade = 14). Degree of urgency classifications also had several drawbacks (overall scores 6–9). Woman-based classifications performed best (scores 5–14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3–8). Conclusions This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary. PMID:21283801

Inter and intra-observer concordance for the diagnosis of portal hypertension gastropathy.

PubMed

Casas, Meritxell; Vergara, Mercedes; Brullet, Enric; Junquera, Félix; Martínez-Bauer, Eva; Miquel, Mireia; Sánchez-Delgado, Jordi; Dalmau, Blai; Campo, Rafael; Calvet, Xavier

2018-03-01

At present there is no fully accepted endoscopic classification for the assessment of the severity of portal hypertensive gastropathy (PHG). Few studies have evaluated inter and intra-observer concordance or the degree of concordance between different endoscopic classifications. To evaluate inter and intra-observer agreement for the presence of portal hypertensive gastropathy and enteropathy using different endoscopic classifications. Patients with liver cirrhosis were included into the study. Enteroscopy was performed under sedation. The location of lesions and their severity was recorded. Images were videotaped and subsequently evaluated independently by three different endoscopists, one of whom was the initial endoscopist. The agreement between observations was assessed using the kappa index. Seventy-four patients (mean age 63.2 years, 53 males and 21 females) were included. The agreement between the three endoscopists regarding the presence or absence of PHG using the Tanoue and McCormack classifications was very low (kappa scores = 0.16 and 0.27, respectively). The current classifications of portal hypertensive gastropathy have a very low degree of intra and inter-observer agreement for the diagnosis and assessment of gastropathy severity.

Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines

NASA Astrophysics Data System (ADS)

Pipaud, Isabel; Lehmkuhl, Frank

2017-09-01

In the field of geomorphology, automated extraction and classification of landforms is one of the most active research areas. Until the late 2000s, this task has primarily been tackled using pixel-based approaches. As these methods consider pixels and pixel neighborhoods as the sole basic entities for analysis, they cannot account for the irregular boundaries of real-world objects. Object-based analysis frameworks emerging from the field of remote sensing have been proposed as an alternative approach, and were successfully applied in case studies falling in the domains of both general and specific geomorphology. In this context, the a-priori selection of scale parameters or bandwidths is crucial for the segmentation result, because inappropriate parametrization will either result in over-segmentation or insufficient segmentation. In this study, we describe a novel supervised method for delineation and classification of alluvial fans, and assess its applicability using a SRTM 1‧‧ DEM scene depicting a section of the north-eastern Mongolian Altai, located in northwest Mongolia. The approach is premised on the application of mean-shift segmentation and the use of a one-class support vector machine (SVM) for classification. To consider variability in terms of alluvial fan dimension and shape, segmentation is performed repeatedly for different weightings of the incorporated morphometric parameters as well as different segmentation bandwidths. The final classification layer is obtained by selecting, for each real-world object, the most appropriate segmentation result according to fuzzy membership values derived from the SVM classification. Our results show that mean-shift segmentation and SVM-based classification provide an effective framework for delineation and classification of a particular landform. Variable bandwidths and terrain parameter weightings were identified as being crucial for consideration of intra-class variability, and, in turn, for a constantly high segmentation quality. Our analysis further reveals that incorporation of morphometric parameters quantifying specific morphological aspects of a landform is indispensable for developing an accurate classification scheme. Alluvial fans exhibiting accentuated composite morphologies were identified as a major challenge for automatic delineation, as they cannot be fully captured by a single segmentation run. There is, however, a high probability that this shortcoming can be overcome by enhancing the presented approach with a routine merging fan sub-entities based on their spatial relationships.

Obtaining the mean relative weights of the cost of care in Catalonia (Spain): retrospective application of the adjusted clinical groups case-mix system in primary health care.

PubMed

Sicras-Mainar, Antoni; Velasco-Velasco, Soledad; Navarro-Artieda, Ruth; Aguado Jodar, Alba; Plana-Ripoll, Oleguer; Hermosilla-Pérez, Eduardo; Bolibar-Ribas, Bonaventura; Prados-Torres, Alejandra; Violan-Fors, Concepción

2013-04-01

The study aims to obtain the mean relative weights (MRWs) of the cost of care through the retrospective application of the adjusted clinical groups (ACGs) in several primary health care (PHC) centres in Catalonia (Spain) in routine clinical practice. This is a retrospective study based on computerized medical records. All patients attended by 13 PHC teams in 2008 were included. The principle measurements were: demographic variables (age and sex), dependent variables (number of diagnoses and total costs), and case-mix or co-morbidity variables (International Classification of Primary Care). The costs model for each patient was established by differentiating the fix costs from the variable costs. In the bivariate analysis, the Student's t, analysis of variance, chi-squared, Pearson's linear correlation and Mann-Whitney-Wilcoxon tests were used. In order to compare the MRW of the present study with those of the United States (US), the concordance [intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC)] and the correlation (coefficient of determination: R²) were measured. The total number of patients studied was 227,235, and the frequentation was 5.9 visits/habitant/year) and with a mean diagnoses number of 4.5 (3.2). The distribution of costs was €148.7 million, of which 29.1% were fixed costs. The mean total cost per patient/year was €654.2 (851.7), which was considered to be the reference MRW. Relationship between study-MRW and US-MRW: ICC was 0.40 [confidential interval (CI) 95%: 0.21-0.60] and the CCC was 0.42 (CI 95%: 0.35-0.49). The correlation between the US MRW and the MRW of the present study can be seen; the adjusted R² value is 0.691. The explanatory power of the ACG classification was 36.9% for the total costs. The R² of the total cost without considering outliers was 56.9%. The methodology has been shown appropriate for promoting the calculation of the MRW for each category of the classification. The results provide a possible practical application in PHC clinical management. © 2012 Blackwell Publishing Ltd.

Classification of co-occurring depression and substance abuse symptoms predicts suicide attempts in adolescents.

PubMed

Effinger, Jenell M; Stewart, David G

2012-08-01

Although both depression and substance use have been found to contribute to suicide attempts, the synergistic impact of these disorders has not been fully explored. Additionally, the impact of subthreshold presentations of these disorders has not been researched. We utilized the Quadrant Model of Classification (a matrix of severity of two disorders) to assess for suicide attempt risk among adolescents. Logistic regression was used to examine the impact of co-occurring disorder classification on suicide risk attempts. Results indicate that quadrant classification had a dramatic impact on suicide attempt risk, with individuals with high severity co-occurring disorders at greatest risk. © 2012 The American Association of Suicidology.

The Predisposing Factors between Dental Caries and Deviations from Normal Weight.

PubMed

Chopra, Amandeep; Rao, Nanak Chand; Gupta, Nidhi; Vashisth, Shelja; Lakhanpal, Manav

2015-04-01

Dental caries and deviations from normal weight are two conditions which share several broadly predisposing factors. So it's important to understand any relationship between dental state and body weight if either is to be managed appropriately. The study was done to find out the correlation between body mass index (BMI), diet, and dental caries among 12-15-year-old schoolgoing children in Panchkula District. A multistage sample of 12-15-year-old school children (n = 810) in Panchkula district, Haryana was considered. Child demographic details and diet history for 5 days was recorded. Data regarding dental caries status was collected using World Health Organization (1997) format. BMI was calculated and categorized according to the World Health Organization classification system for BMI. The data were subjected to statistical analysis using chi-square test and binomial regression developed using the Statistical Package for Social Sciences (SPSS) 20.0. The mean Decayed Missing Filled Teeth (DMFT) score was found to be 1.72 with decayed, missing, and filled teeth to be 1.22, 0.04, and 0.44, respectively. When the sample was assessed based on type of diet, it was found that vegetarians had higher mean DMFT (1.72) as compared to children having mixed diet. Overweight children had highest DMFT (3.21) which was followed by underweight (2.31) and obese children (2.23). Binomial regression revealed that females were 1.293 times at risk of developing caries as compared to males. Fair and poor Simplified-Oral Hygiene Index (OHI-S) showed 3.920 and 4.297 times risk of developing caries as compared to good oral hygiene, respectively. Upper high socioeconomic status (SES) is at most risk of developing caries. Underweight, overweight, and obese are at 2.7, 2.5, and 3 times risk of developing caries as compared to children with normal BMI, respectively. Dental caries and deviations from normal weight are two conditions which share several broadly predisposing factors such as diet, SES, lifestyle and other environmental factors.

The clinical outcomes of deep gray matter injury in children with cerebral palsy in relation with brain magnetic resonance imaging.

PubMed

Choi, Ja Young; Choi, Yoon Seong; Rha, Dong-Wook; Park, Eun Sook

2016-08-01

In the present study we investigated the nature and extent of clinical outcomes using various classifications and analyzed the relationship between brain magnetic resonance imaging (MRI) findings and the extent of clinical outcomes in children with cerebral palsy (CP) with deep gray matter injury. The deep gray matter injuries of 69 children were classified into hypoxic ischemic encephalopathy (HIE) and kernicterus patterns. HIE patterns were divided into four groups (I-IV) based on severity. Functional classification was investigated using the gross motor function classification system-expanded and revised, manual ability classification system, communication function classification system, and tests of cognitive function, and other associated problems. The severity of HIE pattern on brain MRI was strongly correlated with the severity of clinical outcomes in these various domains. Children with a kernicterus pattern showed a wide range of clinical outcomes in these areas. Children with severe HIE are at high risk of intellectual disability (ID) or epilepsy and children with a kernicterus pattern are at risk of hearing impairment and/or ID. Grading severity of HIE pattern on brain MRI is useful for predicting overall outcomes. The clinical outcomes of children with a kernicterus pattern range widely from mild to severe. Delineation of the clinical outcomes of children with deep gray matter injury, which are a common abnormal brain MRI finding in children with CP, is necessary. The present study provides clinical outcomes for various domains in children with deep gray matter injury on brain MRI. The deep gray matter injuries were divided into two major groups; HIE and kernicterus patterns. Our study showed that severity of HIE pattern on brain MRI was strongly associated with the severity of impairments in gross motor function, manual ability, communication function, and cognition. These findings suggest that severity of HIE pattern can be useful for predicting the severity of impairments. Conversely, children with a kernicterus pattern showed a wide range of clinical outcomes in various domains. Children with severe HIE pattern are at high risk of ID or epilepsy and children with kernicterus pattern are at risk of hearing impairment or ID. The strength of our study was the assessment of clinical outcomes after 3 years of age using standardized classification systems in various domains in children with deep gray matter injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

Risk factors affecting injury severity determined by the MAIS score.

PubMed

Ferreira, Sara; Amorim, Marco; Couto, Antonio

2017-07-04

Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score. In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006-2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS. This study showed the impact of variables, such as the presence of blood alcohol, the use of protection devices, the type of crash, and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analyzed as risk factors, showing that elderly and male road users are highly associated with MAIS 3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the magnitude of impact of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification. The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.

Usefulness and applicability of the revised dengue case classification by disease: multi-centre study in 18 countries

PubMed Central

2011-01-01

Background In view of the long term discussion on the appropriateness of the dengue classification into dengue fever (DF), dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), the World Health Organization (WHO) has outlined in its new global dengue guidelines a revised classification into levels of severity: dengue fever with an intermediary group of "dengue fever with warning sings", and severe dengue. The objective of this paper was to compare the two classification systems regarding applicability in clinical practice and surveillance, as well as user-friendliness and acceptance by health staff. Methods A mix of quantitative (prospective and retrospective review of medical charts by expert reviewers, formal staff interviews), semi-quantitative (open questions in staff interviews) and qualitative methods (focus group discussions) were used in 18 countries. Quality control of data collected was undertaken by external monitors. Results The applicability of the DF/DHF/DSS classification was limited, even when strict DHF criteria were not applied (13.7% of dengue cases could not be classified using the DF/DHF/DSS classification by experienced reviewers, compared to only 1.6% with the revised classification). The fact that some severe dengue cases could not be classified in the DF/DHF/DSS system was of particular concern. Both acceptance and perceived user-friendliness of the revised system were high, particularly in relation to triage and case management. The applicability of the revised classification to retrospective data sets (of importance for dengue surveillance) was also favourable. However, the need for training, dissemination and further research on the warning signs was highlighted. Conclusions The revised dengue classification has a high potential for facilitating dengue case management and surveillance. PMID:21510901

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

[Reproducibility of the use of classifications of causes of death in the context of inquiries in perinatal mortality].

PubMed

Rajmil, L; Plasencia, A; Borrell, C

1993-11-01

The objective of this study was to verify the reliability of the classifications of perinatal mortality causes. An independent observer coded the cases of perinatal death (n = 152) collected in the Encuesta Confidencial de Mortalidad Perinatal de Barcelona (ECMP, Confidential Perinatal Mortality Inquiry of Barcelona), by using both the Aberdeen classification system (regarding obstetric factors) and the Wigglesworth classification system (according to the initial pathological cause), with the same information used previously by the ECMP Commission. For the Aberdeen classification, the observed concordance index (Po) was 86% and the Kappa coefficient (K) 0.77 (95% CI: 0.68-0.86). For the Wigglesworth classification, the figures were 89% and 0.82 (95% CI: 0.74-0.90), respectively. The disagreement was mainly due to differences in the interpretation of the sequence of death, minimal information available in order to classify the cause of death, and misunderstanding of the existing information. To a lesser extent, the disagreement was caused by a failure to comply with the rules laid down for classifications. The assessment of the causes of death was not significantly influenced by birth weight, gestational age, time of death or the presence of necropsy. These results support the use of classifications of perinatal mortality causes in the context of confidential inquiries.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

2015-01-01

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems. PMID:26262687

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Convolutional neural network with transfer learning for rice type classification

NASA Astrophysics Data System (ADS)

Patel, Vaibhav Amit; Joshi, Manjunath V.

2018-04-01

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

A fingerprint classification algorithm based on combination of local and global information

NASA Astrophysics Data System (ADS)

Liu, Chongjin; Fu, Xiang; Bian, Junjie; Feng, Jufu

2011-12-01

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

On the Discriminant Analysis in the 2-Populations Case

NASA Astrophysics Data System (ADS)

Rublík, František

2008-01-01

The empirical Bayes Gaussian rule, which in the normal case yields good values of the probability of total error, may yield high values of the maximum probability error. From this point of view the presented modified version of the classification rule of Broffitt, Randles and Hogg appears to be superior. The modification included in this paper is termed as a WR method, and the choice of its weights is discussed. The mentioned methods are also compared with the K nearest neighbours classification rule.

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.

Diagnostic criteria, severity classification and guidelines of localized scleroderma.

PubMed

Asano, Yoshihide; Fujimoto, Manabu; Ishikawa, Osamu; Sato, Shinichi; Jinnin, Masatoshi; Takehara, Kazuhiko; Hasegawa, Minoru; Yamamoto, Toshiyuki; Ihn, Hironobu

2018-04-23

We established diagnostic criteria and severity classification of localized scleroderma because there is no established diagnostic criteria or widely accepted severity classification of the disease. Also, there has been no clinical guideline for localized scleroderma, so we established its clinical guideline ahead of all over the world. In particular, the clinical guideline was established by clinical questions based on evidence-based medicine according to the New Minds Clinical Practice Guideline Creation Manual (version 1.0). We aimed to make the guideline easy to use and reliable based on the newest evidence, and to present guidance as specific as possible for various clinical problems in treatment of localized scleroderma. © 2018 Japanese Dermatological Association.

Gynecomastia Classification for Surgical Management: A Systematic Review and Novel Classification System.

PubMed

Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas

2017-03-01

Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.

Feature weighting using particle swarm optimization for learning vector quantization classifier

NASA Astrophysics Data System (ADS)

Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

Invertebrate Iridoviruses: A Glance over the Last Decade

PubMed Central

Özcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D.; Özgen, Arzu

2018-01-01

Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses; however, novel strains are continuously being discovered and, in many cases, reliable classification has been challenging. Further impeding classification, invertebrate iridoviruses (IIVs) can occasionally infect vertebrates; thus, host range is often not a useful criterion for classification. In this review, we discuss the current classification of iridovirids, focusing on genomic and structural features that distinguish vertebrate and invertebrate iridovirids and viral factors linked to host interactions in IIV6 (Invertebrate iridescent virus 6). In addition, we show for the first time how complete genome sequences of viral isolates can be leveraged to improve classification of new iridovirid isolates and resolve ambiguous relations. Improved classification of the iridoviruses may facilitate the identification of genus-specific virulence factors linked with diverse host phenotypes and host interactions. PMID:29601483

Invertebrate Iridoviruses: A Glance over the Last Decade.

PubMed

İnce, İkbal Agah; Özcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D; Özgen, Arzu

2018-03-30

Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses; however, novel strains are continuously being discovered and, in many cases, reliable classification has been challenging. Further impeding classification, invertebrate iridoviruses (IIVs) can occasionally infect vertebrates; thus, host range is often not a useful criterion for classification. In this review, we discuss the current classification of iridovirids, focusing on genomic and structural features that distinguish vertebrate and invertebrate iridovirids and viral factors linked to host interactions in IIV6 (Invertebrate iridescent virus 6). In addition, we show for the first time how complete genome sequences of viral isolates can be leveraged to improve classification of new iridovirid isolates and resolve ambiguous relations. Improved classification of the iridoviruses may facilitate the identification of genus-specific virulence factors linked with diverse host phenotypes and host interactions.

Empirical Wavelet Transform Based Features for Classification of Parkinson's Disease Severity.

PubMed

Oung, Qi Wei; Muthusamy, Hariharan; Basah, Shafriza Nisha; Lee, Hoileong; Vijean, Vikneswaran

2017-12-29

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

Annual update of data for estimating ESALs : draft.

DOT National Transportation Integrated Search

2008-10-01

A revised procedure for estimating equivalent single axleloads (ESALs) was developed in 1985. This procedure used weight, classification, and traffic volume data collected by the Transportation Cabinet's Division of Planning. : Annual updates of data...

Content Abstract Classification Using Naive Bayes

NASA Astrophysics Data System (ADS)

Latif, Syukriyanto; Suwardoyo, Untung; Aldrin Wihelmus Sanadi, Edwin

2018-03-01

This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

Multilabel user classification using the community structure of online networks

PubMed Central

Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user’s graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score. PMID:28278242

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Shimozaki, Steven S.; Abbey, Craig K.

2002-01-01

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Multilabel user classification using the community structure of online networks.

PubMed

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

PubMed

Banzato, T; Cherubini, G B; Atzori, M; Zotti, A

2018-05-01

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Pseudogynecomastia after massive weight loss: detectability of technique, patient satisfaction, and classification.

PubMed

Gusenoff, Jeffrey A; Coon, Devin; Rubin, J Peter

2008-11-01

An increasing number of male patients are presenting for treatment of male chest deformity after massive weight loss. The authors prefer to preserve the nipple-areola complex on a dermoglandular pedicle. They sought to identify detectability of technique, assess patient satisfaction, and outline a treatment algorithm for this population. Ten male massive weight loss patients underwent chest-contouring procedures over a period of 6 years and were surveyed to identify satisfaction with reconstruction. Preoperative photographs were used to devise a classification system. Twenty-seven medical professionals evaluated and rated digital photographs of the patients. Eight patients had pedicled reconstructions and two had free-nipple grafts. Mean age was 42.9 +/- 9.5 years, mean pre-weight loss body mass index was 54.1 +/- 10.6, post-weight loss body mass index was 29.4 +/- 4.5, and mean change in body mass index was 24.8 +/- 9.7. All patients would have surgery again, nine would recommend it to a friend, six would go shirtless in public, nine reported no loss of nipple sensation, and three reported dysesthesias of the nipple-areola complex. Medical professionals reproducibly associated poor wound healing with free-nipple grafting and rated poorly positioned nipple-areola complexes with low aesthetic scores. Medical professional scores for chest contour and nipple-areola complex aesthetics did not correlate with technique and were lower than scores provided by the patients. Patient satisfaction for treatment of the male chest deformity after massive weight loss is high. In carefully selected patients, preservation of the nipple-areola complex on a dermoglandular pedicle can aid in achieving an optimal aesthetic result.

Utility of ultrasound-guided injection of botulinum toxin type A for muscle imbalance in children with obstetric brachial plexus palsy: Description of the procedure and action protocol.

PubMed

García Ron, A; Gallardo, R; Huete Hernani, B

2017-03-24

Obstetric brachial plexus palsy (OBPP) usually has a favourable prognosis. However, nearly one third of all severe cases have permanent sequelae causing a high level of disability. In this study, we explore the effectiveness of ultrasound-guided injection of botulinum toxin A (BoNT-A) and describe the procedure. We designed a prospective, descriptive study including patients with moderate to severe OBPP who were treated between January 2010 and December 2014. We gathered demographic data, type of OBPP, and progression. Treatment effectiveness was assessed with the Active Movement Scale (AMS), the Mallet classification, and video recordings. We gathered a total of 14 133 newborns, 15 of whom had OBPP (1.6 per 1000 live births). Forty percent of the cases had severe OBPP (0.4/1000), a dystocic delivery, and APGAR scores < 5; mean weight was 4038g. Mean age at treatment onset was 11.5 months. The muscles most frequently receiving BoNT-A injections were the pronator teres, subscapularis, teres major, latissimus dorsi, and pectoralis major. All the patients who completed the follow-up period (83%) experienced progressive improvements: up to 3 points on the AMS and a mean score of 19.5 points out of 25 on the Mallet classification at 2 years. Treatment improved muscle function and abnormal posture in all cases. Surgery was avoided in 3 patients and delayed in one. Adverse events were mild and self-limited. Due to its safety and effectiveness, BoNT-A may be used off-label as an adjuvant to physical therapy and/or surgery in moderate to severe OBPP. Ultrasound may increase effectiveness and reduce adverse effects. Copyright © 2017 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.

Relationships between World Health Organization "International Classification of Functioning, Disability and Health" Constructs and Participation in Adults with Severe Mental Illness

ERIC Educational Resources Information Center

Sánchez, Jennifer; Rosenthal, David A.; Chan, Fong; Brooks, Jessica; Bezyak, Jill L.

2016-01-01

Purpose: To examine the World Health Organization "International Classification of Functioning, Disability and Health" (ICF) constructs as correlates of community participation of people with severe mental illnesses (SMI). Methods: Quantitative descriptive research design using multiple regression and correlational techniques was used to…

[TNM 2010. What's new?].

PubMed

Wittekind, C

2010-10-01

In the seventh edition of the TNM Classification of Malignant Tumours there are several entirely new classifications: upper aerodigestive mucosal melanoma, gastrointestinal stromal tumour, gastrointestinal carcinoid (neuroendocrine tumour), intrahepatic cholangiocarcinoma, Merkel cell carcinoma, uterine sarcomas, and adrenal cortical carcinoma. Significant modifications concern carcinomas of the oesophagus, oesophagogastric junction, stomach, appendix, biliary tract, lung, skin, prostate and ophthalmic tumours, which will be not addressed in this article. For several tumour entities only minor changes were introduced which might be of importance in daily practice. The new classifications and changes will be commented on without going into details.

How recalibration method, pricing, and coding affect DRG weights

PubMed Central

Carter, Grace M.; Rogowski, Jeannette A.

1992-01-01

We compared diagnosis-related group (DRG) weights calculated using the hospital-specific relative-value (HSR V) methodology with those calculated using the standard methodology for each year from 1985 through 1989 and analyzed differences between the two methods in detail for 1989. We provide evidence suggesting that classification error and subsidies of higher weighted cases by lower weighted cases caused compression in the weights used for payment as late as the fifth year of the prospective payment system. However, later weights calculated by the standard method are not compressed because a statistical correlation between high markups and high case-mix indexes offsets the cross-subsidization. HSR V weights from the same files are compressed because this methodology is more sensitive to cross-subsidies. However, both sets of weights produce equally good estimates of hospital-level costs net of those expenses that are paid by outlier payments. The greater compression of the HSR V weights is counterbalanced by the fact that more high-weight cases qualify as outliers. PMID:10127456

The Pfirrmann classification of lumbar intervertebral disc degeneration: an independent inter- and intra-observer agreement assessment.

PubMed

Urrutia, Julio; Besa, Pablo; Campos, Mauricio; Cikutovic, Pablo; Cabezon, Mario; Molina, Marcelo; Cruz, Juan Pablo

2016-09-01

Grading inter-vertebral disc degeneration (IDD) is important in the evaluation of many degenerative conditions, including patients with low back pain. Magnetic resonance imaging (MRI) is considered the best imaging instrument to evaluate IDD. The Pfirrmann classification is commonly used to grade IDD; the authors describing this classification showed an adequate agreement using it; however, there has been a paucity of independent agreement studies using this grading system. The aim of this study was to perform an independent inter- and intra-observer agreement study using the Pfirrmann classification. T2-weighted sagittal images of 79 patients consecutively studied with lumbar spine MRI were classified using the Pfirrmann grading system by six evaluators (three spine surgeons and three radiologists). After a 6-week interval, the 79 cases were presented to the same evaluators in a random sequence for repeat evaluation. The intra-class correlation coefficient (ICC) and the weighted kappa (wκ) were used to determine the inter- and intra-observer agreement. The inter-observer agreement was excellent, with an ICC = 0.94 (0.93-0.95) and wκ = 0.83 (0.74-0.91). There were no differences between spine surgeons and radiologists. Likewise, there were no differences in agreement evaluating the different lumbar discs. Most differences among observers were only of one grade. Intra-observer agreement was also excellent with ICC = 0.86 (0.83-0.89) and wκ = 0.89 (0.85-0.93). In this independent study, the Pfirrmann classification demonstrated an adequate agreement among different observers and by the same observer on separate occasions. Furthermore, it allows communication between radiologists and spine surgeons.

Domain Adaptation for Alzheimer’s Disease Diagnostics

PubMed Central

Wachinger, Christian; Reuter, Martin

2016-01-01

With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy. PMID:27262241

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Classification of Airflow Limitation Based on z-Score Underestimates Mortality in Patients with Chronic Obstructive Pulmonary Disease.

PubMed

Tejero, Elena; Prats, Eva; Casitas, Raquel; Galera, Raúl; Pardo, Paloma; Gavilán, Adelaida; Martínez-Cerón, Elisabet; Cubillos-Zapata, Carolina; Del Peso, Luis; García-Río, Francisco

2017-08-01

Global Lung Function Initiative recommends reporting lung function measures as z-score, and a classification of airflow limitation (AL) based on this parameter has recently been proposed. To evaluate the prognostic capacity of the AL classifications based on z-score or percentage predicted of FEV 1 in patients with chronic obstructive pulmonary disease (COPD). A cohort of 2,614 patients with COPD recruited outside the hospital setting was examined after a mean (± SD) of 57 ± 13 months of follow-up, totaling 10,322 person-years. All-cause mortality was analyzed, evaluating the predictive capacity of several AL staging systems. Based on Global Initiative for Chronic Obstructive Lung Disease guidelines, 461 patients (17.6%) had mild, 1,452 (55.5%) moderate, 590 (22.6%) severe, and 111 (4.2%) very severe AL. According to z-score classification, 66.3% of patients remained with the same severity, whereas 23.7% worsened and 10.0% improved. Unlike other staging systems, patients with severe AL according to z-score had higher mortality than those with very severe AL (increase of risk by 5.2 and 3.9 times compared with mild AL, respectively). The predictive capacity for 5-year survival was slightly higher for FEV 1 expressed as percentage of predicted than as z-score (area under the curve: 0.714-0.760 vs. 0.649-0.708, respectively). A severity-dependent relationship between AL grades by z-score and mortality was only detected in patients younger than age 60 years. In patients with COPD, the AL classification based on z-score predicts worse mortality than those based on percentage of predicted. It is possible that the z-score underestimates AL severity in patients older than 60 years of age with severe functional impairment.

Comparison of Neural Networks and Tabular Nearest Neighbor Encoding for Hyperspectral Signature Classification in Unresolved Object Detection

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.; Key, R.

Accurate and computationally efficient spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications using linear mixing models, signature classification accuracy depends on accurate spectral endmember discrimination [1]. If the endmembers cannot be classified correctly, then the signatures cannot be classified correctly, and object recognition from hyperspectral data will be inaccurate. In practice, the number of endmembers accurately classified often depends linearly on the number of inputs. This can lead to potentially severe classification errors in the presence of noise or densely interleaved signatures. In this paper, we present an comparison of emerging technologies for nonimaging spectral signature classfication based on a highly accurate, efficient search engine called Tabular Nearest Neighbor Encoding (TNE) [3,4] and a neural network technology called Morphological Neural Networks (MNNs) [5]. Based on prior results, TNE can optimize its classifier performance to track input nonergodicities, as well as yield measures of confidence or caution for evaluation of classification results. Unlike neural networks, TNE does not have a hidden intermediate data structure (e.g., the neural net weight matrix). Instead, TNE generates and exploits a user-accessible data structure called the agreement map (AM), which can be manipulated by Boolean logic operations to effect accurate classifier refinement algorithms. The open architecture and programmability of TNE's agreement map processing allows a TNE programmer or user to determine classification accuracy, as well as characterize in detail the signatures for which TNE did not obtain classification matches, and why such mis-matches occurred. In this study, we will compare TNE and MNN based endmember classification, using performance metrics such as probability of correct classification (Pd) and rate of false detections (Rfa). As proof of principle, we analyze classification of multiple closely spaced signatures from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to Bayesian techniques based on classical neural networks. [1] Winter, M.E. "Fast autonomous spectral end-member determination in hyperspectral data," in Proceedings of the 13th International Conference On Applied Geologic Remote Sensing, Vancouver, B.C., Canada, pp. 337-44 (1999). [2] N. Keshava, "A survey of spectral unmixing algorithms," Lincoln Laboratory Journal 14:55-78 (2003). [3] Key, G., M.S. SCHMALZ, F.M. Caimi, and G.X. Ritter. "Performance analysis of tabular nearest neighbor encoding algorithm for joint compression and ATR", in Proceedings SPIE 3814:115-126 (1999). [4] Schmalz, M.S. and G. Key. "Algorithms for hyperspectral signature classification in unresolved object detection using tabular nearest neighbor encoding" in Proceedings of the 2007 AMOS Conference, Maui HI (2007). [5] Ritter, G.X., G. Urcid, and M.S. Schmalz. "Autonomous single-pass endmember approximation using lattice auto-associative memories", Neurocomputing (Elsevier), accepted (June 2008).

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

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

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

Assessment of Interradiologist Agreement Regarding Mammographic Breast Density Classification Using the Fifth Edition of the BI-RADS Atlas.

PubMed

Ekpo, Ernest U; Ujong, Ujong Peter; Mello-Thoms, Claudia; McEntee, Mark F

2016-05-01

The objective of the present study was to assess interradiologist agreement regarding mammographic breast density assessment performed using the rating scale outlined in the fifth edition of the BI-RADS atlas of the American College of Radiology. Breast density assessments of 1000 cases were conducted by five radiologists from the same institution who together had recently undergone retraining in mammographic breast density classification based on the fifth edition of BI-RADS. The readers assigned breast density grades (A-D) on the basis of the BI-RADS classification scheme. Repeat assessment of 100 cases was performed by all readers 1 month after the initial assessment. A weighted kappa was used to calculate intrareader and interreader agreement. Intrareader agreement ranged from a kappa value of 0.86 (95% CI, 0.77-0.93) to 0.89 (95% CI, 0.81-0.95) on a four-category scale (categories A-D) and from 0.89 (95% CI, 0.86-0.92) to 0.94 (95% CI, 0.89-0.97) on a two-category scale (category A-B vs category C-D). Interreader agreement ranged from substantial (κ = 0.76; 95% CI, 0.73-0.78) to almost perfect (κ = 0.87; 95% CI, 0.86-0.89) on a four-category scale, and the overall weighted kappa value was substantial (0.79; 95% CI, 0.78-0.83). Interreader agreement on a two-category scale ranged from a kappa value of 0.85 (95% CI, 0.83-0.86) to 0.91 (95% CI, 0.90-0.92), and the overall weighted kappa was 0.88 (95% CI, 0.87-0.89). Overall, with regard to mammographic breast density classification, radiologists had substantial interreader agreement when a four-category scale was used and almost perfect interreader agreement when a dichotomous scale was used.

[Study on biopharmaceutics classification system for Chinese materia medica of extract of Huanglian].

PubMed

Liu, Yang; Yin, Xiu-Wen; Wang, Zi-Yu; Li, Xue-Lian; Pan, Meng; Li, Yan-Ping; Dong, Ling

2017-11-01

One of the advantages of biopharmaceutics classification system of Chinese materia medica (CMMBCS) is expanding the classification research level from single ingredient to multi-components of Chinese herb, and from multi-components research to holistic research of the Chinese materia medica. In present paper, the alkaloids of extract of huanglian were chosen as the main research object to explore their change rules in solubility and intestinal permeability of single-component and multi-components, and to determine the biopharmaceutical classification of extract of Huanglian from holistic level. The typical shake-flask method and HPLC were used to detect the solubility of single ingredient of alkaloids from extract of huanglian. The quantitative research of alkaloids in intestinal absorption was measured in single-pass intestinal perfusion experiment while permeability coefficient of extract of huanglian was calculated by self-defined weight coefficient method. Copyright© by the Chinese Pharmaceutical Association.

Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.

76 FR 72355 - Classification of Two Steroids, Prostanozol and Methasterone, as Schedule III Anabolic Steroids...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-23

... positive nitrogen balance and protein metabolism, resulting in increases in protein synthesis and lean body... nitrogen balance and androgenic activity based on weight changes of the ventral prostrate of prostanozol...

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2014 CFR

2014-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2012 CFR

2012-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

40 CFR 88.311-93 - Emissions standards for Inherently Low-Emission Vehicles.

Code of Federal Regulations, 2013 CFR

2013-07-01

... section depending on the vehicle's weight classification. (ii) The vehicle shall be certified as having... class shall have exhaust emissions which do not exceed the exhaust emission standards in grams per brake...

Face classification using electronic synapses

NASA Astrophysics Data System (ADS)

Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H.-S. Philip; Qian, He

2017-05-01

Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

Face classification using electronic synapses.

PubMed

Yao, Peng; Wu, Huaqiang; Gao, Bin; Eryilmaz, Sukru Burc; Huang, Xueyao; Zhang, Wenqiang; Zhang, Qingtian; Deng, Ning; Shi, Luping; Wong, H-S Philip; Qian, He

2017-05-12

Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system.

Degree Classification and Recent Graduates' Ability: Is There Any Signalling Effect?

ERIC Educational Resources Information Center

Di Pietro, Giorgio

2017-01-01

Research across several countries has shown that degree classification (i.e. the final grade awarded to students successfully completing university) is an important determinant of graduates' first destination outcome. Graduates leaving university with higher degree classifications have better employment opportunities and a higher likelihood of…

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

PubMed

Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

2018-03-19

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Developing a classification system of social communication functioning of preschool children with autism spectrum disorder.

PubMed

Di Rezze, Briano; Rosenbaum, Peter; Zwaigenbaum, Lonnie; Hidecker, Mary Jo Cooley; Stratford, Paul; Cousins, Martha; Camden, Chantal; Law, Mary

2016-09-01

Impairments in social communication are the hallmark of autism spectrum disorder (ASD). Operationalizing 'severity' in ASD has been challenging; thus, stratifying by functioning has not been possible. The purpose of this study is to describe the development of the Autism Classification System of Functioning: Social Communication (ACSF:SC) and to evaluate its consistency within and between parent and professional ratings. (1) ACSF:SC development based on focus groups and surveys involving parents, educators, and clinicians familiar with preschoolers with ASD; and (2) evaluation of the intra- and interrater agreement of the ACSF:SC using weighted kappa (кw ). Seventy-six participants were involved in the development process. Core characteristics of social communication were ascertained: communicative intent; communicative skills and reciprocity; and impact of environment. Five ACSF:SC levels were created and content-validated across participants. Best capacity and typical performance agreement ratings varied as follows: intrarater agreement on 41 children was кw =0.61 to 0.69 for parents, and кw =0.71 to 0.95 for professionals; interrater agreement between professionals was кw =0.47 to 0.61, and between parents and professionals was кw =0.33 to 0.53. Perspectives from parents and professionals informed ACSF:SC development, providing common descriptions of the levels of everyday communicative abilities of children with ASD to complement the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Rater agreement demonstrates that the ACSF:SC can be used with acceptable consistency compared with other functional classification systems. © 2016 Mac Keith Press.

Distinguishing body mass and activity level from the lower limb: can entheses diagnose obesity?

PubMed

Godde, Kanya; Taylor, Rebecca Wilson

2013-03-10

The ability to estimate body size from the skeleton has broad applications, but is especially important to the forensic community when identifying unknown skeletal remains. This research investigates the utility of using entheses/muscle skeletal markers of the lower limb to estimate body size and to classify individuals into average, obese, and active categories, while using a biomechanical approach to interpret the results. Eighteen muscle attachment sites of the lower limb, known to be involved in the sit-to-stand transition, were scored for robusticity and stress in 105 white males (aged 31-81 years) from the William M. Bass Donated Skeletal Collection. Both logistic regression and log linear models were applied to the data to (1) test the utility of entheses as an indicator of body weight and activity level, and (2) to generate classification percentages that speak to the accuracy of the method. Thirteen robusticity scores differed significantly between the groups, but classification percentages were only slightly greater than chance. However, clear differences could be seen between the average and obese and the average and active groups. Stress scores showed no value in discriminating between groups. These results were interpreted in relation to biomechanical forces at the microscopic and macroscopic levels. Even though robusticity alone is not able to classify individuals well, its significance may show greater value when incorporated into a model that has multiple skeletal indicators. Further research needs to evaluate a larger sample and incorporate several lines of evidence to improve classification rates. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Evidence and Implications of Mortality Associated with Acute Plasmodium vivax Malaria

PubMed Central

2013-01-01

Vivax malaria threatens patients despite relatively low-grade parasitemias in peripheral blood. The tenet of death as a rare outcome, derived from antiquated and flawed clinical classifications, disregarded key clinical evidence, including (i) high rates of mortality in neurosyphilis patients treated with vivax malaria; (ii) significant mortality from zones of endemicity; and (iii) the physiological threat inherent in repeated, very severe paroxysms in any patient, healthy or otherwise. The very well-documented course of this infection, with the exception of parasitemia, carries all of the attributes of “perniciousness” historically linked to falciparum malaria, including severe disease and fatal outcomes. A systematic analysis of the parasite biomass in severely ill patients that includes blood, marrow, and spleen may ultimately explain this historic misunderstanding. Regardless of how this parasite is pernicious, recent data demonstrate that the infection comes with a significant burden of morbidity and associated mortality. The extraordinary burden of malaria is not heavily weighted upon any single continent by a single species of parasite—it is a complex problem for the entire endemic world, and both species are of fundamental importance. Humanity must rally substantial resources, intellect, and energy to counter this daunting but profound threat. PMID:23297258

Prevalence of class-I, class-II and class-III obesity in Australian adults between 1995 and 2011-12.

PubMed

Keating, Catherine; Backholer, Kathryn; Gearon, Emma; Stevenson, Christopher; Swinburn, Boyd; Moodie, Marj; Carter, Rob; Peeters, Anna

2015-01-01

To compare the prevalence of class-I, II and III obesity in Australian adults between 1995, 2007-08 and 2011-12. Prevalence data for adults (aged 18+ years) were sourced from customised data from the nationally representative National Nutrition Survey (1995), the National Health Survey (2007-08), and the Australian Health Survey (2011-12) conducted by the Australian Bureau of Statistics. Obesity classifications were based on measured height and weight (class-I body mass index: 30.0-34.9 kg/m(2), class-II: 35.0-39.9 kg/m(2) and class-III: ≥ 40.0 kg/m(2)). Severe obesity was defined as class-II or class-III obesity. Between 1995 and 2011-12, the prevalence of obesity (all classes combined) increased from 19.1% to 27.2%. During this 17 year period, relative increases in class I, II and III obesity were 1.3, 1.7 and 2.2-fold respectively. In 2011-12, the prevalence of class I, II and III obesity was 19.4, 5.9 and 2.0 per cent respectively in men, and 16.1, 6.9 and 4.2 per cent respectively in women. One in every ten people was severely obese, increasing from one in twenty in 1995, and women were disproportionally represented in this population. Obesity prevalence increased with increasing levels of area-level socioeconomic disadvantage, particularly for the more severely obese classes. Severe obesity affected 6.2% and 13.4% in the least and most disadvantaged quintiles respectively. Over the last two decades, there have been substantial increases in the prevalence of obesity, particularly the more severe levels of obesity. This study highlights high risk groups who warrant targeted weight gain prevention interventions. Copyright © 2015 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Sexual Dimorphism Analysis and Gender Classification in 3D Human Face

NASA Astrophysics Data System (ADS)

Hu, Yuan; Lu, Li; Yan, Jingqi; Liu, Zhi; Shi, Pengfei

In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method.This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

PubMed Central

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

PubMed

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

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

Hartantyo, Eddy, E-mail: hartantyo@ugm.ac.id; Brotopuspito, Kirbani S.; Sismanto

The liquefactions phenomena have been reported after a shocking 6.5Mw earthquake hit Yogyakarta province in the morning at 27 May 2006. Several researchers have reported the damage, casualties, and soil failure due to the quake, including the mapping and analyzing the liquefaction phenomena. Most of them based on SPT test. The study try to draw the liquefaction susceptibility by means the shear velocity profiling using modified Multichannel Analysis of Surface Waves (MASW). This paper is a preliminary report by using only several measured MASW points. The study built 8-channel seismic data logger with 4.5 Hz geophones for this purpose. Several differentmore » offsets used to record the high and low frequencies of surface waves. The phase-velocity diagrams were stacked in the frequency domain rather than in time domain, for a clearer and easier dispersion curve picking. All codes are implementing in Matlab. From these procedures, shear velocity profiling was collected beneath each geophone’s spread. By mapping the minimum depth of shallow water table, calculating PGA with soil classification, using empirical formula for saturated soil weight from shear velocity profile, and calculating CRR and CSR at every depth, the liquefaction characteristic can be identify in every layer. From several acquired data, a liquefiable potential at some depth below water table was obtained.« less

Validation of the Japanese disease severity classification and the GAP model in Japanese patients with idiopathic pulmonary fibrosis.

PubMed

Kondoh, Shun; Chiba, Hirofumi; Nishikiori, Hirotaka; Umeda, Yasuaki; Kuronuma, Koji; Otsuka, Mitsuo; Yamada, Gen; Ohnishi, Hirofumi; Mori, Mitsuru; Kondoh, Yasuhiro; Taniguchi, Hiroyuki; Homma, Sakae; Takahashi, Hiroki

2016-09-01

The clinical course of idiopathic pulmonary fibrosis (IPF) shows great inter-individual differences. It is important to standardize the severity classification to accurately evaluate each patient׳s prognosis. In Japan, an original severity classification (the Japanese disease severity classification, JSC) is used. In the United States, the new multidimensional index and staging system (the GAP model) has been proposed. The objective of this study was to evaluate the model performance for the prediction of mortality risk of the JSC and GAP models using a large cohort of Japanese patients with IPF. This is a retrospective cohort study including 326 patients with IPF in the Hokkaido prefecture from 2003 to 2007. We obtained the survival curves of each stage of the GAP and JSC models to perform a comparison. In the GAP model, the prognostic value for mortality risk of Japanese patients was also evaluated. In the JSC, patient prognoses were roughly divided into two groups, mild cases (Stages I and II) and severe cases (Stages III and IV). In the GAP model, there was no significant difference in survival between Stages II and III, and the mortality rates in the patients classified into the GAP Stages I and II were underestimated. It is difficult to predict accurate prognosis of IPF using the JSC and the GAP models. A re-examination of the variables from the two models is required, as well as an evaluation of the prognostic value to revise the severity classification for Japanese patients with IPF. Copyright © 2016 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Adverse events following cervical manipulative therapy: consensus on classification among Dutch medical specialists, manual therapists, and patients.

PubMed

Kranenburg, Hendrikus A; Lakke, Sandra E; Schmitt, Maarten A; Van der Schans, Cees P

2017-12-01

To obtain consensus-based agreement on a classification system of adverse events (AE) following cervical spinal manipulation. The classification system should be comprised of clear definitions, include patients' and clinicians' perspectives, and have an acceptable number of categories. Design : A three-round Delphi study. Participants : Thirty Dutch participants (medical specialists, manual therapists, and patients) participated in an online survey. Procedure : Participants inventoried AE and were asked about their preferences for either a three- or a four-category classification system. The identified AE were classified by two analysts following the International Classification of Functioning, Disability and Health (ICF), and the International Classification of Diseases and Related Health Problems (ICD-10). Participants were asked to classify the severity for all AE in relation to the time duration. Consensus occurred in a three-category classification system. There was strong consensus for 16 AE in all severities (no, minor, and major AE) and all three time durations [hours, days, weeks]. The 16 AE included anxiety, flushing, skin rash, fainting, dizziness, coma, altered sensation, muscle tenderness, pain, increased pain during movement, radiating pain, dislocation, fracture, transient ischemic attack, stroke, and death. Mild to strong consensus was reached for 13 AE. A consensus-based classification system of AE is established which includes patients' and clinicians' perspectives and has three categories. The classification comprises a precise description of potential AE in accordance with internationally accepted classifications. After international validation, clinicians and researchers may use this AE classification system to report AE in clinical practice and research.

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.

A rapid method to visualize von willebrand factor multimers by using agarose gel electrophoresis, immunolocalization and luminographic detection.

PubMed

Krizek, D R; Rick, M E

2000-03-15

A highly sensitive and rapid clinical method for the visualization of the multimeric structure of von Willebrand Factor in plasma and platelets is described. The method utilizes submerged horizontal agarose gel electrophoresis, followed by transfer of the von Willebrand Factor onto a polyvinylidine fluoride membrane, and immunolocalization and luminographic visualization of the von Willebrand Factor multimeric pattern. This method distinguishes type 1 from types 2A and 2B von Willebrand disease, allowing timely evaluation and classification of von Willebrand Factor in patient plasma. It also allows visualization of the unusually high molecular weight multimers present in platelets. There are several major advantages to this method including rapid processing, simplicity of gel preparation, high sensitivity to low concentrations of von Willebrand Factor, and elimination of radioactivity.

Applications of Some Artificial Intelligence Methods to Satellite Soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.

1985-01-01

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

Multivariate classification of the infrared spectra of cell and tissue samples

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

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

NASA Astrophysics Data System (ADS)

Jamshidpour, N.; Homayouni, S.; Safari, A.

2017-09-01

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

46 CFR 298.11 - Vessel requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... with accepted commercial experience and practice. (g) Metric Usage. Our preferred system of measurement and weights for Vessels and Shipyard Projects is the metric system. ...), classification societies to be ISO 9000 series registered or Quality Systems Certificate Scheme qualified IACS...

46 CFR 298.11 - Vessel requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

... with accepted commercial experience and practice. (g) Metric Usage. Our preferred system of measurement and weights for Vessels and Shipyard Projects is the metric system. ...), classification societies to be ISO 9000 series registered or Quality Systems Certificate Scheme qualified IACS...

46 CFR 298.11 - Vessel requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... with accepted commercial experience and practice. (g) Metric Usage. Our preferred system of measurement and weights for Vessels and Shipyard Projects is the metric system. ...), classification societies to be ISO 9000 series registered or Quality Systems Certificate Scheme qualified IACS...

46 CFR 298.11 - Vessel requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

... with accepted commercial experience and practice. (g) Metric Usage. Our preferred system of measurement and weights for Vessels and Shipyard Projects is the metric system. ...), classification societies to be ISO 9000 series registered or Quality Systems Certificate Scheme qualified IACS...

46 CFR 298.11 - Vessel requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

... with accepted commercial experience and practice. (g) Metric Usage. Our preferred system of measurement and weights for Vessels and Shipyard Projects is the metric system. ...), classification societies to be ISO 9000 series registered or Quality Systems Certificate Scheme qualified IACS...

Casemix classification payment for sub-acute and non-acute inpatient care, Thailand.

PubMed

Khiaocharoen, Orathai; Pannarunothai, Supasit; Zungsontiporn, Chairoj; Riewpaiboon, Wachara

2010-07-01

There is a need to develop other casemix classifications, apart from DRG for sub-acute and non-acute inpatient care payment mechanism in Thailand. To develop a casemix classification for sub-acute and non-acute inpatient service. The study began with developing a classification system, analyzing cost, assigning payment weights, and ended with testing the validity of this new casemix system. Coefficient of variation, reduction in variance, linear regression, and split-half cross-validation were employed. The casemix for sub-acute and non-acute inpatient services contained 98 groups. Two percent of them had a coefficient of variation of the cost of higher than 1.5. The reduction in variance of cost after the classification was 32%. Two classification variables (physical function and the rehabilitation impairment categories) were key determinants of the cost (adjusted R2 = 0.749, p = .001). Validity results of split-half cross-validation of sub-acute and non-acute inpatient service were high. The present study indicated that the casemix for sub-acute and non-acute inpatient services closely predicted the hospital resource use and should be further developed for payment of the inpatients sub-acute and non-acute phase.

Design of partially supervised classifiers for multispectral image data

NASA Technical Reports Server (NTRS)

Jeon, Byeungwoo; Landgrebe, David

1993-01-01

A partially supervised classification problem is addressed, especially when the class definition and corresponding training samples are provided a priori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class characteristics of all classes in a given data set. Considering the effort in both time and man-power required to have a well-defined, exhaustive list of classes with a corresponding representative set of training samples, this 'partially' supervised capability would be very desirable, assuming adequate classifier performance can be obtained. Two different classification algorithms are developed to achieve simplicity in classifier design by reducing the requirement of prior statistical information without sacrificing significant classifying capability. The first one is based on optimal significance testing, where the optimal acceptance probability is estimated directly from the data set. In the second approach, the partially supervised classification is considered as a problem of unsupervised clustering with initially one known cluster or class. A weighted unsupervised clustering procedure is developed to automatically define other classes and estimate their class statistics. The operational simplicity thus realized should make these partially supervised classification schemes very viable tools in pattern classification.

A new methodology for monitoring wood fluxes in rivers using a ground camera: Potential and limits

NASA Astrophysics Data System (ADS)

Benacchio, Véronique; Piégay, Hervé; Buffin-Bélanger, Thomas; Vaudor, Lise

2017-02-01

Ground imagery, which produces large amounts of valuable data at high frequencies, is increasingly used by fluvial geomorphologists to survey and understand processes. While such technology provides immense quantities of information, it can be challenging to analyze and requires automatization and associated development of new methodologies. This paper presents a new approach to automate the processing of image analysis to monitor wood delivery from the upstream Rhône River (France). The Génissiat dam is used as an observation window; all pieces of wood coming from the catchment are trapped here, hence a wood raft accumulates over time. In 2011, we installed an Axis 211W camera to acquire oblique images of the reservoir every 10 min with the goal of automatically detecting a wood raft area, in order to transform it to wood weight (t) and flux (t/d). The methodology we developed is based on random forest classification to detect the wood raft surface over time, which provided a good classification rate of 97.2%. Based on 14 mechanical wood extractions that included weight of wood removed each time, conducted during the survey period, we established a relationship between wood weight and wood raft surface area observed just before the extraction (R2 = 0.93). We found that using such techniques to continuously monitor wood flux is difficult because the raft undergoes very significant changes through time in terms of density, with a very high interday and intraday variability. Misclassifications caused by changes in weather conditions can be mitigated as well as errors from variation in pixel resolution (owing to camera position or window size), but a set of effects on raft density and mobility must still be explored (e.g., dam operation effects, wind on the reservoir surface). At this stage, only peak flow contribution to wood delivery can be well calculated, but determining an accurate, continuous series of wood flux is not possible. Several recommendations are made in terms of maximizing the potential benefit of such monitoring.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

PubMed Central

2011-01-01

Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool. PMID:21609459

Seizure classification in EEG signals utilizing Hilbert-Huang transform.

PubMed

Oweis, Rami J; Abdulhay, Enas W

2011-05-24

Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

Phenotypic characterization of glioblastoma identified through shape descriptors

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

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

Effect of slice thickness on brain magnetic resonance image texture analysis

PubMed Central

2010-01-01

Background The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients. Methods We averaged the intensities of three consecutive 1-mm slices to simulate 3-mm slices. Two hundred sixty-four texture parameters were calculated for both the original and the averaged slices. Wilcoxon's signed ranks test was used to find differences between the regions of interest representing white matter and multiple sclerosis plaques. Linear and nonlinear discriminant analyses were applied with several separate training and test sets to determine the actual classification accuracy. Results Only moderate differences in distributions of the texture parameter value for 1-mm and simulated 3-mm-thick slices were found. Our study also showed that white matter areas are well separable from multiple sclerosis plaques even if the slice thickness differs between training and test sets. Conclusions Three-millimeter-thick magnetic resonance image slices acquired with a 1.5 T clinical magnetic resonance scanner seem to be sufficient for texture analysis of multiple sclerosis plaques and white matter tissue. PMID:20955567

Structure/activity relationships for biodegradability and their role in environmental assessment

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

Boethling, R.S.

1994-12-31

Assessment of biodegradability is an important part of the review process for both new and existing chemicals under the Toxic Substances Control Act. It is often necessary to estimate biodegradability because experimental data are unavailable. Structure/biodegradability relationships (SBR) are a means to this end. Quantitative SBR have been developed, but this approach has not been very useful because they apply only to a few narrowly defined classes of chemicals. In response to the need for more widely applicable methods, multivariate analysis has been used to develop biodegradability classification models. For example, recent efforts have produced four new models. Two calculatemore » the probability of rapid biodegradation and can be used for classification; the other two models allow semi-quantitative estimation of primary and ultimate biodegradation rates. All are based on multiple regressions against 36 preselected substructures plus molecular weight. Such efforts have been fairly successful by statistical criteria, but in general are hampered by a lack of large and consistent datasets. Knowledge-based expert systems may represent the next step in the evolution of SBR. In principle such systems need not be as severely limited by imperfect datasets. However, the codification of expert knowledge and reasoning is a critical prerequisite. Results of knowledge acquisition exercises and modeling based on them will also be described.« less

Dewey Decimal Classification for U. S. Conn: An Advantage?

ERIC Educational Resources Information Center

Marek, Kate

This paper examines the use of the Dewey Decimal Classification (DDC) system at the U. S. Conn Library at Wayne State College (WSC) in Nebraska. Several developments in the last 20 years which have eliminated the trend toward reclassification of academic library collections from DDC to the Library of Congress (LC) classification scheme are…

Is there any correlation between the ATS, BTS, ERS and GOLD COPD's severity scales and the frequency of hospital admissions?

PubMed

Tsoumakidou, Maria; Tzanakis, Nikolaos; Voulgaraki, Olga; Mitrouska, Ioanna; Chrysofakis, Georgios; Samiou, Maria; Siafakas, Nikolaos M

2004-02-01

Disagreement exists between different COPD guidelines considering classification of severity of the disease. The aim of our study was to determine whether there is any correlation between severity scales of various COPD guidelines (ATS, BTS, ERS and GOLD) and the frequency of hospitalisations for COPD exacerbation. A cohort of 67 COPD patients (65 male 2 female, 45 ex-smokers, 22 current smokers, aged (69.4 +/- 1.1)) was recruited from those admitted in the pulmonary clinic of the University Hospital of Heraklion, Crete for an acute exacerbation. Lung function tests and arterial blood gases analyses were performed during stable conditions at a scheduled visit 2 months after discharge. The patients were stratified using the FEV1 percent-predicted measurement of this visit into mild, moderate and severe in accordance to the ATS, BTS, ERS and GOLD scales of severity. The number of hospitalisations for acute exacerbation was recorded for the following 18 months. A total of 165 exacerbations were recorded. The correlation between the severity of COPD and the number of hospitalisations per year was statistically significant using the GOLD classification system of severity (P = 0.02 and r = 0.294). A weak correlation was also found between the number of hospitalisations and the ERS classification system (P = 0.05 and r = 0.24). No statistically significant correlation was found between the number of hospitalisations and the ATS or BTS severity scales. In conclusion the GOLD and ERS classification systems of severity of COPD correlated to exacerbations causing hospitalisation. The same was not true for the ATS and BTS severity scales. Better correlation was achieved with the GOLD scale.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Knee X-ray image analysis method for automated detection of Osteoarthritis

PubMed Central

Shamir, Lior; Ling, Shari M.; Scott, William W.; Bos, Angelo; Orlov, Nikita; Macura, Tomasz; Eckley, D. Mark; Ferrucci, Luigi; Goldberg, Ilya G.

2008-01-01

We describe a method for automated detection of radiographic Osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal and moderate). Image analysis is performed by first identifying a set of image content descriptors and image transforms that are informative for the detection of OA in the X-rays, and assigning weights to these image features using Fisher scores. Then, a simple weighted nearest neighbor rule is used in order to predict the KL grade to which a given test X-ray sample belongs. The dataset used in the experiment contained 350 X-ray images classified manually by their KL grades. Experimental results show that moderate OA (KL grade 3) and minimal OA (KL grade 2) can be differentiated from normal cases with accuracy of 91.5% and 80.4%, respectively. Doubtful OA (KL grade 1) was detected automatically with a much lower accuracy of 57%. The source code developed and used in this study is available for free download at www.openmicroscopy.org. PMID:19342330

Classification of brain tumours using short echo time 1H MR spectra

NASA Astrophysics Data System (ADS)

Devos, A.; Lukas, L.; Suykens, J. A. K.; Vanhamme, L.; Tate, A. R.; Howe, F. A.; Majós, C.; Moreno-Torres, A.; van der Graaf, M.; Arús, C.; Van Huffel, S.

2004-09-01

The purpose was to objectively compare the application of several techniques and the use of several input features for brain tumour classification using Magnetic Resonance Spectroscopy (MRS). Short echo time 1H MRS signals from patients with glioblastomas ( n = 87), meningiomas ( n = 57), metastases ( n = 39), and astrocytomas grade II ( n = 22) were provided by six centres in the European Union funded INTERPRET project. Linear discriminant analysis, least squares support vector machines (LS-SVM) with a linear kernel and LS-SVM with radial basis function kernel were applied and evaluated over 100 stratified random splittings of the dataset into training and test sets. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of binary classifiers, while the percentage of correct classifications was used to evaluate the multiclass classifiers. The influence of several factors on the classification performance has been tested: L2- vs. water normalization, magnitude vs. real spectra and baseline correction. The effect of input feature reduction was also investigated by using only the selected frequency regions containing the most discriminatory information, and peak integrated values. Using L2-normalized complete spectra the automated binary classifiers reached a mean test AUC of more than 0.95, except for glioblastomas vs. metastases. Similar results were obtained for all classification techniques and input features except for water normalized spectra, where classification performance was lower. This indicates that data acquisition and processing can be simplified for classification purposes, excluding the need for separate water signal acquisition, baseline correction or phasing.

Acute pancreatitis.

PubMed

Talukdar, Rupjyoti; Vege, Santhi S

2015-09-01

To summarize recent data on classification systems, cause, risk factors, severity prediction, nutrition, and drug treatment of acute pancreatitis. Comparison of the Revised Atlanta Classification and Determinant Based Classification has shown heterogeneous results. Simvastatin has a protective effect against acute pancreatitis. Young black male, alcohol, smoldering symptoms, and subsequent diagnosis of chronic pancreatitis are risk factors associated with readmissions after acute pancreatitis. A reliable clinical or laboratory marker or a scoring system to predict severity is lacking. The PYTHON trial has shown that oral feeding with on demand nasoenteric tube feeding after 72 h is as good as nasoenteric tube feeding within 24 h in preventing infections in predicted severe acute pancreatitis. Male sex, multiple organ failure, extent of pancreatic necrosis, and heterogeneous collection are factors associated with failure of percutaneous drainage of pancreatic collections. The newly proposed classification systems of acute pancreatitis need to be evaluated more critically. New biomarkers are needed for severity prediction. Further well designed studies are required to assess the type of enteral nutritional formulations for acute pancreatitis. The optimal minimally invasive method or combination to debride the necrotic collections is evolving. There is a great need for a drug to treat the disease early on to prevent morbidity and mortality.

Robust prediction of protein subcellular localization combining PCA and WSVMs.

PubMed

Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

2011-08-01

Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Parental recognition of overweight in school-age children.

PubMed

West, Delia S; Raczynski, James M; Phillips, Martha M; Bursac, Zoran; Heath Gauss, C; Montgomery, Brooke E E

2008-03-01

Examine the accuracy of parental weight perceptions of overweight children before and after the implementation of childhood obesity legislation that included BMI screening and feedback. Statewide telephone surveys of parents of overweight (BMI > or = 85th percentile) Arkansas public school children before (n = 1,551; 15% African American) and after (n = 2,508; 15% African American) policy implementation were examined for correspondence between parental perception of child's weight and objective classification. Most (60%) parents of overweight children underestimated weight at baseline. Parents of younger children were significantly more likely to underestimate (65%) than parents of adolescents (51%). Overweight parents were not more likely to underestimate, nor was inaccuracy associated with parental education or socioeconomic status. African-American parents were twice as likely to underestimate as whites. One year after BMI screening and feedback was implemented, the accuracy of classification of overweight children improved (53% underestimation). African-American parents had significantly greater improvements than white parents (P < 0.0001). Parental recognition of childhood overweight may be improved with BMI screening and feedback, and African-American parents may specifically benefit. Nonetheless, underestimation of overweight is common and may have implications for public health interventions.

The definition of severe and early-onset preeclampsia. Statements from the International Society for the Study of Hypertension in Pregnancy (ISSHP).

PubMed

Tranquilli, Andrea L; Brown, Mark A; Zeeman, Gerda G; Dekker, Gustaaf; Sibai, Baha M

2013-01-01

There is discrepancy in the literature on the definitions of severe and early-onset pre-eclampsia. We aimed to determine those definitions for clinical purposes and to introduce them in the classification of the hypertensive disorders of pregnancy for publication purposes. We circulated a questionnaire to the International Committee of the International Society for the Study of Hypertension in Pregnancy focusing on the thresholds for defining severe preeclampsia and the gestation at which to define early-onset preeclampsia, and on the definition and inclusion of the HELLP syndrome or other clinical features in severe preeclampsia. The questions were closed, but all answers had space for more open detailed comments. There was a general agreement to define preeclampsia as severe if blood pressure was >160mmHg systolic or 110mmHg diastolic. There was scarce agreement on the amount of proteinuria to define severity. The HELLP syndrome was considered a feature to include in the severe classification. Most investigators considered early-onset preeclampsia as that occurring before 34weeks. A definition of pre-eclampsia is paramount for driving good clinical practice. Classifications on the other hand are useful to enable international comparisons of clinical data and outcomes. We used the results of this survey to update our previous classification for the purposes of providing clinical research definitions of severe and early onset pre-eclampsia that will hopefully be accepted in the international literature. Copyright © 2012 International Society for the Study of Hypertension in Pregnancy. All rights reserved.

Relationship between the surrogate anthropometric measures, foot length and chest circumference and birth weight among newborns of Sarlahi, Nepal

PubMed Central

Mullany, LC; Darmstadt, GL; Khatry, SK; LeClerq, SC; Tielsch, JM

2008-01-01

Background Classification of infants into low birth weight (LBW, <2500 g) or very low birth weight (VLBW, <2000 g) categories is a crucial step in targeting interventions to high-risk infants. Objective To compare the validity of chest circumference and foot length as surrogate anthropometric measures for the identification of LBW and VLBW infants. Subjects and setting Newborn infants (n = 1640) born between March and June 2004 in 30 Village Development Committees of Sarlahi district, Nepal. Design Chest circumference, foot length and weight (SECA 727, precise to 2 g) of newborns were measured within 72 h after birth. The sensitivity, specificity and predictive values for a range of cutoff points of the anthropometric measures were estimated using the digital scale measurements as the gold standard. Results Among LBW infants (469/1640, 28.6%), chest circumference measures <30.3 cm were 91% sensitive and 83% specific. Similar levels of sensitivity for foot length were achieved only with considerable loss of specificity (<45%). Foot length measurements <6.9 cm were 88% sensitive and 86% specific for the identification of VLBW infants. Conclusion Chest circumference was superior to foot length in classification of infants into birth weight categories. For the identification of VLBW infants, foot length performed well, and may be preferable to chest circumference, as the former measure does not require removal of infant swaddling clothes. In the absence of more precise direct measures of birth weight, chest circumference is recommended over foot length for the identification of LBW infants. Sponsorship The National Institute of Child Health and Human Development; the Saving Newborn Lives Initiative, Save the Children – US and the Office of Heath and Nutrition, United States Agency for International Development (see Acknowledgements). PMID:16885929

Classification images for localization performance in ramp-spectrum noise.

PubMed

Abbey, Craig K; Samuelson, Frank W; Zeng, Rongping; Boone, John M; Eckstein, Miguel P; Myers, Kyle

2018-05-01

This study investigates forced localization of targets in simulated images with statistical properties similar to trans-axial sections of x-ray computed tomography (CT) volumes. A total of 24 imaging conditions are considered, comprising two target sizes, three levels of background variability, and four levels of frequency apodization. The goal of the study is to better understand how human observers perform forced-localization tasks in images with CT-like statistical properties. The transfer properties of CT systems are modeled by a shift-invariant transfer function in addition to apodization filters that modulate high spatial frequencies. The images contain noise that is the combination of a ramp-spectrum component, simulating the effect of acquisition noise in CT, and a power-law component, simulating the effect of normal anatomy in the background, which are modulated by the apodization filter as well. Observer performance is characterized using two psychophysical techniques: efficiency analysis and classification image analysis. Observer efficiency quantifies how much diagnostic information is being used by observers to perform a task, and classification images show how that information is being accessed in the form of a perceptual filter. Psychophysical studies from five subjects form the basis of the results. Observer efficiency ranges from 29% to 77% across the different conditions. The lowest efficiency is observed in conditions with uniform backgrounds, where significant effects of apodization are found. The classification images, estimated using smoothing windows, suggest that human observers use center-surround filters to perform the task, and these are subjected to a number of subsequent analyses. When implemented as a scanning linear filter, the classification images appear to capture most of the observer variability in efficiency (r 2 = 0.86). The frequency spectra of the classification images show that frequency weights generally appear bandpass in nature, with peak frequency and bandwidth that vary with statistical properties of the images. In these experiments, the classification images appear to capture important features of human-observer performance. Frequency apodization only appears to have a significant effect on performance in the absence of anatomical variability, where the observers appear to underweight low spatial frequencies that have relatively little noise. Frequency weights derived from the classification images generally have a bandpass structure, with adaptation to different conditions seen in the peak frequency and bandwidth. The classification image spectra show relatively modest changes in response to different levels of apodization, with some evidence that observers are attempting to rebalance the apodized spectrum presented to them. © 2018 American Association of Physicists in Medicine.

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback

PubMed Central

Cisler, Josh M.; Bush, Keith; James, G. Andrew; Smitherman, Sonet; Kilts, Clinton D.

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD. PMID:26241958

Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set.

PubMed

Adler, Werner; Gefeller, Olaf; Gul, Asma; Horn, Folkert K; Khan, Zardad; Lausen, Berthold

2016-12-07

Random forests are successful classifier ensemble methods consisting of typically 100 to 1000 classification trees. Ensemble pruning techniques reduce the computational cost, especially the memory demand, of random forests by reducing the number of trees without relevant loss of performance or even with increased performance of the sub-ensemble. The application to the problem of an early detection of glaucoma, a severe eye disease with low prevalence, based on topographical measurements of the eye background faces specific challenges. We examine the performance of ensemble pruning strategies for glaucoma detection in an unbalanced data situation. The data set consists of 102 topographical features of the eye background of 254 healthy controls and 55 glaucoma patients. We compare the area under the receiver operating characteristic curve (AUC), and the Brier score on the total data set, in the majority class, and in the minority class of pruned random forest ensembles obtained with strategies based on the prediction accuracy of greedily grown sub-ensembles, the uncertainty weighted accuracy, and the similarity between single trees. To validate the findings and to examine the influence of the prevalence of glaucoma in the data set, we additionally perform a simulation study with lower prevalences of glaucoma. In glaucoma classification all three pruning strategies lead to improved AUC and smaller Brier scores on the total data set with sub-ensembles as small as 30 to 80 trees compared to the classification results obtained with the full ensemble consisting of 1000 trees. In the simulation study, we were able to show that the prevalence of glaucoma is a critical factor and lower prevalence decreases the performance of our pruning strategies. The memory demand for glaucoma classification in an unbalanced data situation based on random forests could effectively be reduced by the application of pruning strategies without loss of performance in a population with increased risk of glaucoma.

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback.

PubMed

Cisler, Josh M; Bush, Keith; James, G Andrew; Smitherman, Sonet; Kilts, Clinton D

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD.

Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

PubMed Central

Shamonin, Denis P.; Bron, Esther E.; Lelieveldt, Boudewijn P. F.; Smits, Marion; Klein, Stefan; Staring, Marius

2013-01-01

Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license. PMID:24474917

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Ecosystem Services Linking People to Coastal Habitats ...

EPA Pesticide Factsheets

Background/Question/Methods: There is a growing need to incorporate and prioritize ecosystem services/condition information into land-use decision making. While there are a number of place-based studies looking at how land-use decisions affect the availability and delivery of coastal services, many of these methods require data, funding and/or expertise that may be inaccessible to many coastal communities. Using existing classification standards for beneficiaries and coastal habitats, (i.e., Final Ecosystem Goods and Services Classification System (FEGS-CS) and Coastal and Marine Ecological Classification Standard (CMECS)), a comprehensive literature review was coupled with a “weight of evidence” approach to evaluate linkages between beneficiaries and coastal habitat features most relevant to community needs. An initial search of peer-reviewed journal articles was conducted using JSTOR and ScienceDirect repositories identifying sources that provide evidence for coastal beneficiary:habitat linkages. Potential sources were further refined based on a double-blind review of titles, abstracts, and full-texts, when needed. Articles in the final list were then scored based on habitat/beneficiary specificity and data quality (e.g., indirect evidence from literature reviews was scored lower than direct evidence from case studies with valuation results). Scores were then incorporated into a weight of evidence framework summarizing the support for each benefici

Tissue discrimination in magnetic resonance imaging of the rotator cuff

NASA Astrophysics Data System (ADS)

Meschino, G. J.; Comas, D. S.; González, M. A.; Capiel, C.; Ballarin, V. L.

2016-04-01

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients’ health.

Detecting suicidality among adolescent outpatients: evaluation of trained clinicians' suicidality assessment against a structured diagnostic assessment made by trained raters.

PubMed

Holi, Matti Mikael; Pelkonen, Mirjami; Karlsson, Linnea; Tuisku, Virpi; Kiviruusu, Olli; Ruuttu, Titta; Marttunen, Mauri

2008-12-31

Accurate assessment of suicidality is of major importance. We aimed to evaluate trained clinicians' ability to assess suicidality against a structured assessment made by trained raters. Treating clinicians classified 218 adolescent psychiatric outpatients suffering from a depressive mood disorder into three classes: 1-no suicidal ideation, 2-suicidal ideation, no suicidal acts, 3-suicidal or self-harming acts. This classification was compared with a classification with identical content derived from the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-PL) made by trained raters. The convergence was assessed by kappa- and weighted kappa tests. The clinicians' classification to class 1 (no suicidal ideation) was 85%, class 2 (suicidal ideation) 50%, and class 3 (suicidal acts) 10% concurrent with the K-SADS evaluation (gamma2 = 37.1, df 4, p = 0.000). Weighted kappa for the agreement of the measures was 0.335 (CI = 0.198-0.471, p < 0.0001). The clinicians under-detected suicidal and self-harm acts, but over-detected suicidal ideation. There was only a modest agreement between the trained clinicians' suicidality evaluation and the K-SADS evaluation, especially concerning suicidal or self-harming acts. We suggest a wider use of structured scales in clinical and research settings to improve reliable detection of adolescents with suicidality.

Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles.

PubMed

Yukinawa, Naoto; Oba, Shigeyuki; Kato, Kikuya; Ishii, Shin

2009-01-01

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the "optimal coding problem," has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.

Near-Earth object hazardous impact: A Multi-Criteria Decision Making approach.

PubMed

Sánchez-Lozano, J M; Fernández-Martínez, M

2016-11-16

The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Predictive factors of difficulty in lower third molar extraction: A prospective cohort study

PubMed Central

Alvira-González, Joaquín; Valmaseda-Castellón, Eduard; Quesada-Gómez, Carmen; Gay-Escoda, Cosme

2017-01-01

Background Several publications have measured the difficulty of third molar removal, trying to establish the main risk factors, however several important preoperative and intraoperative variables are overlooked. Material and Methods A prospective cohort study comprising a total of 130 consecutive lower third molar extractions was performed. The outcome variables used to measure the difficulty of the extraction were operation time and a 100mm visual analogue scale filled by the surgeon at the end of the surgical procedure. The predictors were divided into 4 different groups (demographic, anatomic, radiographic and operative variables). A descriptive, bivariate and multivariate analysis of the data was performed. Results Patients’ weight, the presence of bulbous roots, the need to perform crown and root sectioning of the lower third molar and Pell and Gregory 123 classification significantly influenced both outcome variables (p< 0.05). Conclusions Certain anatomical, radiological and operative variables appear to be important factors in the assessment of surgical difficulty in the extraction of lower third molars. Key words:Third molar, surgical extraction, surgical difficulty. PMID:27918736

A Comparison of Computer-Based Classification Testing Approaches Using Mixed-Format Tests with the Generalized Partial Credit Model

ERIC Educational Resources Information Center

Kim, Jiseon

2010-01-01

Classification testing has been widely used to make categorical decisions by determining whether an examinee has a certain degree of ability required by established standards. As computer technologies have developed, classification testing has become more computerized. Several approaches have been proposed and investigated in the context of…

ASTM and other specifications and classifications for petroleum products and lubricants. Fifth edition

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

Not Available

1989-01-01

This book includes specifications and classifications from ASTM committees on paint and related coatings and materials; road and paving materials; wood; roofing, waterproofing and bituminous materials; rubber; soaps and other detergents; aromatic hydrocarbons and related chemicals; and electrical insulating liquids and gases. Also included are several related, important specifications and classifications from other organizations.

A systematic review of the Robson classification for caesarean section: what works, doesn't work and how to improve it.

PubMed

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A Metin; Torloni, Maria Regina

2014-01-01

Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation.

A Systematic Review of the Robson Classification for Caesarean Section: What Works, Doesn't Work and How to Improve It

PubMed Central

Betrán, Ana Pilar; Vindevoghel, Nadia; Souza, Joao Paulo; Gülmezoglu, A. Metin; Torloni, Maria Regina

2014-01-01

Background Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Methods Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. Results 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. Conclusions The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation. PMID:24892928