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PubMedDate not supplied

College students classified with ADHD and the foreign language requirement.

The conventional assumption of most disability service providers is that students classified as having attention-deficit/hyperactivity disorder (ADHD) will experience difficulties in foreign language (FL) courses. However, the evidence in support of this assumption is anecdotal. In this empirical investigation, the demographic profiles, overall academic performance, college entrance scores, and FL classroom performance of 68 college students classified as having ADHD were examined. All students had graduated from the same university over a 5-year period. The findings showed that all 68 students had completed the university's FL requirement by passing FL courses. The students' college entrance scores were similar to the middle 50% of freshmen at this university, and their graduating grade point average was similar to the typical graduating senior at the university. The students had participated in both lower (100) and upper (200, 300, 400) level FL courses and had achieved mostly average and above-average grades (A, B, C) in these courses. One student had majored and eight students had minored in an FL. Two thirds of the students passed all of their FL courses without the use of instructional accommodations. In this study, the classification of ADHD did not appear to interfere with participants' performance in FL courses. The findings suggest that students classified as having ADHD should enroll in and fulfill the FL requirement by passing FL courses.

Adult↗

PubMed2004

Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier.

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier.

Bayes Theorem↗

PubMed2006

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Algorithms↗

PubMed2006

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

BACKGROUND: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. METHODS: We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. RESULTS: Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. CONCLUSION: A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

Abstracting and Indexing↗

PubMed2006

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

MOTIVATION: Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. RESULTS: The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. CONCLUSION: We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Cluster Analysis↗

PubMed2006

Mining housekeeping genes with a Naive Bayes classifier.

BACKGROUND: Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce. RESULTS: In this work, a Naive Bayes classifier based only on physical and functional characteristics of genes already available in databases, like exon length and measures of chromatin compactness, has achieved a 97% success rate in classification of human housekeeping genes (93% for mouse and 90% for fruit fly). CONCLUSION: The newly obtained lists of housekeeping and tissue specific genes adhere to the expected functions and tissue expression patterns for the two classes. Overall, the classifier shows promise, and in the future additional attributes might be included to improve its discriminating power.

Animals↗

PubMed2026

A cfDNA fragmentomics classifier for noninvasive differentiation of benign and malignant renal masses.

Noninvasive differentiation of malignant and benign renal masses remains a major clinical challenge, particularly for radiologically indeterminate lesions. Here, we developed and validated a plasma cell-free DNA (cfDNA) fragmentomics-based machine learning classifier for renal mass characterization. The model was trained on 331 participants (171 cancer, 160 benign) and independently validated on 144 participants (73 cancer, 71 benign). Three cfDNA fragmentation features, including copy number variation (CNV), fragmentation-based methylation (FRAGMA), and nucleosome footprint (NF), derived from low-pass whole-genome sequencing, were integrated into an ensemble framework. The model achieved strong discriminative performance, with area under the curve (AUC) values of 0.956 in the training cohort and 0.946 in the validation cohort, outperforming individual feature-based models. At a predefined operating threshold corresponding to 90% sensitivity, specificity reached 0.90 and 0.87, respectively. Notably, most cancer samples exhibited low tumor fraction (TF < 3%), yet the model maintained robust performance in low-TF samples (AUCs: 0.952 and 0.941, respectively). Performance remained consistent across tumor stage, grade, and histological subtypes. The classifier also demonstrated potential clinical utility in diagnostically challenging settings, including lipid-poor angiomyolipoma and oncocytoma, with 12 of 13 oncocytoma samples correctly classified in an independent cohort. In addition, the model correctly identified 85.3% of benign masses > 4 cm, for which surgical intervention is more commonly considered, and 84.6% of malignant tumors ≤ 4 cm, for which management can be challenging. Collectively, these findings support cfDNA fragmentomics as a promising noninvasive liquid biopsy approach for renal mass evaluation and clinical decision-making.

Humans↗

PubMed1992

A neural network chromosome classifier.

We present a chromosome classifier for automated karyotyping of banded chromosomes which uses a multi-layer perception neural network. Two network configurations have been investigated. The resulting classifiers have been trained and tested on data from three different data sets covering a range of data quality. Classification results compare very favourably with those obtained using a highly optimised parametric classifier.

Chromosomes↗

PubMed2001

Naming, defining, and classifying in mental retardation.

Recent discussions about changing the term mental retardation to a different term may be considered in the broader framework of three distinct but related processes: naming (terminology), defining, and classifying. The three processes are analyzed according to their purposes and functions: In naming, a term is assigned; in defining, the term is explained; and in classifying, the group is divided into subgroups according to stated principles. The current status of each process is described, especially as represented in the 1992 AAMR manual, Mental Retardation: Definition, Classification, and Systems of Supports. We suggest three sets of guiding questions that may help evaluate proposed changes in naming, defining, or classifying.

Humans↗

PubMed2003

Polynomial distance classifier correlation filter for pattern recognition.

We introduce what is to our knowledge a new nonlinear shift-invariant classifier called the polynomial distance classifier correlation filter (PDCCF). The underlying theory extends the original linear distance classifier correlation filter [Appl. Opt. 35, 3127 (1996)] to include nonlinear functions of the input pattern. This new filter provides a framework (for combining different classification filters) that takes advantage of the individual filter strengths. In this new filter design, all filters are optimized jointly. We demonstrate the advantage of the new PDCCF method using simulated and real multi-class synthetic aperture radar images.

Journal Article↗

PubMedDate not supplied

Identifying and classifying children with chronic conditions using administrative data with the clinical risk group classification system.

OBJECTIVE: To identify and categorize children with chronic health conditions using administrative data. METHODS: The Clinical Risk Groups (CRGs) system is used to classify children, aged 0-18 years, in a mid-sized health plan into mutually exclusive categories and severity groups. Enrollees are categorized into 9 health status groups--healthy, significant acute, and 7 chronic conditions--and are then stratified by severity. Utilization is examined by category and severity level based on eligibility and claims files for calendar year 1999. Only children enrolled for at least 6 months (newborns at least 3 months) are included. RESULTS: This analysis of 34544 children classifies 85.2% as healthy, including 19.6% with no claims; 5.2% with a significant acute illness; 4.6% with a minor chronic condition; and 4.9% with a moderate to catastrophic chronic condition. The average number of unique medical care encounters per child increases by chronic condition category and by severity level. Compared to national prevalence norms for selected conditions, CRGs do well in identifying patients who have conditions that require interaction with the health care system. CONCLUSIONS: CRGs are a useful tool for identifying, classifying, and stratifying children with chronic health conditions. Enrollees can be grouped into categories for patient tracking, case management, and utilization.

Adolescent↗

PubMed2005

Four commonly used dual-energy X-ray absorptiometry scanners do not identically classify subjects for osteopenia or osteoporosis by T-score in four bone regions BIz11.5.

We investigated whether four commonly used dual-energy X-ray absorptiometry (DXA) scanners (DPX, DPX-L,and Prodigy by GE Lunar, and Delphi-A by Hologic) could classify identical subjects as osteopenic or osteoporotic using the T-score for bone mineral density in four regions of interest: PA spine (L1-L4), femur (total), forearm (total),and 33% radius in 77 adults (38 females) free of treatment for bone metabolic disease (age range 20-81 yr). There were no significant differences between T-score means for posterior-anterior spine by DPX, DPX-L, and Prodigy, but they were higher than the mean T-score by Delphi-A (p < 0.05). Prodigy gave the lowest and DPX and Delphi-A gave the highest T-score for 33% radius (p < 0.05). No subject was classified as osteoporotic in the femur region by the four scanners, although other classifications varied by region and scanner. No two scanners classified subjects identically for osteopenia in any of the four regions. These results indicate that classification of bone density in individual subjects using T-scores varies by different DXA scanners, even the scanners were made by the same manufacturer.

Absorptiometry, Photon↗

PubMed2007

Assessing observer agreement when describing and classifying functioning with the International Classification of Functioning, Disability and Health.

OBJECTIVE: The International Classification of Functioning, Disability and Health (ICF) is used increasingly to describe and classify functioning in medicine without being a psychometrically sound measure. All categories of the ICF are quantified using the same generic 0-4 scale. The objective of this study was to assess observer agreement when describing and classifying functioning with the ICF. DESIGN: A second-level category of the ICF, d430 lifting and carrying objects, was used as an example. To the qualifiers of this category, clinically meaningful definitions were assigned. Data were collected in a cross-sectional survey with repeated measurement. We report raw, specific and chance-corrected measures or agreement, a graphical method and the results of log-linear models for ordinal agreement. SUBJECTS/PATIENTS: A convenience sample of patients requiring physical therapy in an acute hospital. RESULTS: Twenty-five patients were assessed twice by 2 observers. Raw agreement was 0.52. Kappa was 0.36, indicating fair agreement. Different hierarchical log-linear models indicated that the strength of agreement was not homogeneous over all categories. CONCLUSION: Observer agreement has to be evaluated when describing and classifying functioning using the ICF Qualifiers'scale. When assessing inter-observer reliability, the first step is to calculate a summary statistic. Modelling agreement yields valuable insight into the structure of a contingency table, which can lead to further improvement of the scale.

Cross-Sectional Studies↗

PubMed2000

Classifying postures of freely moving rodents with the help of Fourier descriptors and a neural network.

A computerized method for classifying the postures of freely moving rodents is presented. The behavior of the rats was recorded on videotape by means of a camera hanging perpendicular to an open field. An automatic tracking system (10 images/sec) was used to transform the video images of postures into a binary image, thereby providing silhouettes in a computer format. The contours of these silhouettes were used for determining their characteristic features with the help of a Fourier transformation. The resulting features were classified with the help of a Kohonen network composed of 32 neurons. The four best winning neurons, rather than the usual one, were used for the classification. The resolution (11,090 distinct classes of postures), reliability (96.9%), and validity of this method were determined. With the use of the same approach, the effectiveness of this method for classifying behaviors was illustrated by analyzing grooming (247 grooming images vs. 4,950 nongrooming images). We found 15.4% false positives and 2.5% false negatives.

Animals↗

PubMed1999

Why race is differentially classified on U.S. birth and infant death certificates: an examination of two hypotheses.

Among U.S. infants who die within a year of birth, classification of race on birth and death certificates may differ. I investigate two hypotheses: (1) The race of infants of different-race parents is more likely to be differentially classified at birth and death than the race of infants of same-race parents. (2) States with a greater proportion of infant deaths of a given race are less likely to differentially classify infants of that race on birth and death certificates than states with a smaller proportion of infant deaths of that race. Using the Linked Birth/Infant Death data tape for 1983-1985, I assessed the first hypothesis by comparing rates of differential classification for infants with different-race parents and same-race parents. To assess the second hypothesis, I examined the correlations between the proportion of infant deaths of each race in each state and the proportion of infants of that race consistently classified. Differential racial classification on birth and death certificates was more than 31 times as likely with different-race than with same-race parents. The second hypothesis was confirmed for white, black, American Indian, and Japanese infants. As the U.S. population becomes more heterogeneous, attention to these methodologic issues becomes increasingly critical for the measurement and redress of differential racial health status.

Birth Certificates↗

PubMed2000

Can we classify medical data dictionaries?

Medical Data Dictionaries enable a clinical information system to maintain a controlled vocabulary, to store descriptive knowledge about terms, to map between those terms and from those terms to external classifications. They support a variety of functions in the information system, ranging from structured documentation to knowledgebased functions. This paper derives a multi-axial classification for medical data dictionaries. Dictionaries are classified along 4 axes, a vocabulary axis defining vocabulary properties, an application axis which characterises the degree of linkage between dictionary and information system, a semantic axis defining the quality of inter-term relationships and finally a language axis which classifies rules for inter-term relationships in semiotic theory. As an example two existing dictionaries are classified in the model and reference is taken to the design of future dictionaries.

Artificial Intelligence↗

PubMed2001

[Investigation on relation between Yang Shangshan and the classified compilation of Tai su (Comprehensive Notes)].

Taisu, a book complied by Yang Shangshan by imperial decree is the earliest complete commentary and classified complation of Neijing (The Yellow Emperor's Canon of Internal Medicine) now extant. Recently, some scholars argued about the classified compilation of Taisu performed by Yang Shangshan. However, by analyzing and researching into the text and commentary of Taisu, conclusion can be drawn that the classified compilation of Taisu was indeed complied by Yang Shangshan himself. First, "youben" (certain edition) and "yiben" (an edition) in the commentary are not referring to the other editions of Taisu. Second, quotations cited from Su wen (Plain Questions) and Jiu juan (Nine Volumes) in the commentary are not used to collate Taisu. Thire, analysis made on the compilation and commentary by Yang Shangshan. Fourth, analyzing "taisu jing lun (Classical Discourses of Taisu)", the commentary of Taisu Shui lun (On water in Comprehensive Notes).

China↗

PubMed2002

"I could tell you, but then I'd have to kill you": classified information in the psychiatric evaluation.

Psychiatrists and other mental health professionals are presented with special challenges when their patients are involved in covert operations or other matters of national security. The patients' involvement may, by legal necessity, limit disclosures during the evaluation. Such situations may be encountered with varying degrees of frequency by military psychiatrists or consultants to various federal or law enforcement agencies involved in classified or undercover activities. The need to assess relevant psychosocial stressors while avoiding prohibited disclosure, the legal requirements to report potentially adverse information, or the procedure to gain legal permission to discuss classified details may present novel challenges for therapists in such evaluations. In this article, we present a case report illustrating these challenges and review applicable regulations and public law governing the disclosure of classified information. We also discuss common pitfalls and strategies for handling such situations.

Access to Information↗