PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Classification Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources
PubMedDate not supplied

[Numerical taxonomy of the genus Desulfovibrio by group analysis].

The Desulfovibrio genus has a particular interest because it includes the microorganisms connected with the corrosion produced microbiologically. The taxonomy of the genus shows disadvantages due to its metabolical and physiological characteristics. In this paper, 14 strains of the Desulfovibrio type were studied from the metabolical point of view. Numeric taxonomy was carried out according to the Group Analysis method, using and comparing the change possibilities of the method. The Consensus Method was also applied. The results obtained indicate a low metabolic activity of the strains with regard to the number of compounds which can be used as energy source. The taxonomic method showed a better structure with more clear divisions, corresponding to Simple Matching coefficient (which coincides with other symmetric coefficients and with the distance coefficient) with average bond (UPGMA). It is estimated that the present classification will vary in time with new strains with different metabolic characteristics. The two groups of bacteria correspond to those with more and less degrading ability.

Algorithms↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMedDate not supplied

Structure analysis and classification of cervical cells using a processing system based on TV.

This paper presents preliminary results of a cell classification experiment using a new approach for feature extraction. The algorithm takes into account the special requirements of a fast parallel processing system (processor-oriented algorithms). A cell image is described by several hundred features derived from the nucleus only. The most significant features with respect to classification are determined by statistical analysis. Applying principal axis transform, a new feature set is computed, reduced considerably in dimensions. The data base (1,925 cell images of Papanicolaou-stained cervical specimens) was divided into a training set (963 images) and a test set (962 images). The classification results of the test set show that the recognition rate for the two-class problem (normal, suspicious) is better than 91%, using only ten morphologic features.

Cervix Mucus↗
PubMed1975

Evaluation and selection of optimal solvents and solvent combinations in thin-layer chromatography. Application of the method to basic drugs.

A series of simple mathematical techniques for the evaluation of solvents and solvent combinations in thin-layer chromatography have been investigated. A strategy for the rapid selection of the optimum combination is proposed. It uses classification procedures based on calculation of the similarity between systems. The classification is carried out using a simple graph-theoretical procedure (Kruskal's algorithm) or numerical taxonomy. The selection of optimal sets from the clusters which appear in the classification is based on the information content as derived from Shannon's equation. The method has been applied to an RF data set for basic drugs. It is concluded that these methods indeed allow the selection of optimal systems or combination of systems.

Chromatography, Thin Layer↗
PubMed1991

Continuous personnel scheduling algorithms: a literature review.

Hospitals frequently use personnel scheduling options as recruiting and retention instruments. The successful application of these personnel scheduling tools, whether developed in-house or purchased from vendors, requires appreciation of the interrelationships of three basic manpower decisions--staffing, personnel scheduling, and allocation. This article introduces these basic relationships and their influence on the development of satisfactory personnel schedules. Next, it reviews published personnel scheduling algorithms, applicable to hospital operations, within the context of the three manpower decisions. It is proposed that scheduling algorithms be classified by type of schedule produced (cyclic or noncyclic); and technique used (heuristic, mathematical programming, or self-scheduling). The characteristics of each classification are discussed. Considerations for the development of new personnel scheduling algorithms are also presented.

Algorithms↗
PubMed2025

acmgscaler: an R package and Colab for standardized gene-level variant effect score calibration within the ACMG/AMP framework.

MOTIVATION: A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. RESULTS: Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce acmgscaler, a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification. AVAILABILITY AND IMPLEMENTATION: The R package and Colab notebook are available at https://github.com/badonyi/acmgscaler.

Software↗
PubMedDate not supplied

In vivo myeloarchitectonic analysis of human striate and extrastriate cortex using magnetic resonance imaging.

A primary goal of investigations into the organization of human cerebral cortex is to determine the functional specificity of architectonic regions. This includes the correlation of neurobehavioral deficits with neuropathological data for clinical diagnosis and treatment, and the identification of active brain regions using functional neural imaging techniques, such as positron emission tomography, electroencephalographic and magnetoencephalographic (EEG and MEG) source localization algorithms, and direct cortical stimulation. Currently, the architectonic classification of a cortical region identified by these methods is inferred from the comparison of its cerebral topographic position to cytoarchitectonic brain atlases. However, substantial intersubject variability in the position of cytoarchitectonic regions with respect to cerebral topographic landmarks may lead to errors in this procedure. An alternative method is presented here, which uses magnetic resonance (MR) imaging to identify myeloarchitectonic regions of isocortex directly by estimating the relative concentration of myelin within cortical laminae. This high-resolution MR protocol is used to identify striate cortex (Brodmann's area 17) and extrastriate cortex in vivo. Correspondence of MR signal intensity with myeloarchitectonic data from a postmortem brain confirms this identification. As MR imaging technology improves, this noninvasive method has the potential to identify and discriminate among at least 50 cortical regions in the living human brain.

Female↗
PubMed1992

Assessing and understanding patient risk.

Nonsteroidal anti-inflammatory drug (NSAID) gastropathy is the most frequent and one of the most severe drug side effects in the United States. NSAID-associated gastropathy has been estimated to account for at least 7600 deaths and 76000 hospitalizations each year in the United States alone. Hospitalizations in rheumatoid arthritis patients occurred in 1.6% of patients; for patients with osteoarthritis the incidence appears to be substantially lower. This is based on a consecutive series of 3000 patients with rheumatoid arthritis who were followed prospectively for an average of five years by ARAMIS, the Arthritis, Rheumatism and Aging Medical Information System. Multivariate analyses assessing risk factors for serious gastrointestinal (GI) events were performed on 1694 rheumatoid arthritis patients taking NSAIDs. The most important risk factors of higher age, use of prednisone, previous NSAID GI side effects, prior GI hospitalization, functional disability (based on the American Rheumatism Association classification), and NSAID dose are variables in an algorithm which estimates the risk of a serious GI event occurring in the next 12 months. Knowledge of risk factors and their interrelationships provides a tool for identifying patients at high risk and guides therapeutic decisions.

Aging↗
PubMed1991

Vector dissimilarity and clustering.

Based on the description of objects by m attributes, an m-element vector dissimilarity function is defined that, unlike scalar functions, retains the distinction among attributes. This function, which satisfies the conditions for a metric, allows the definition of betweenness, which can then be used for clustering. Applications to the subset-generation phase of conditional clustering and to nearest-neighbor-type algorithms are described.

Classification↗
PubMed1992

A nationwide survey of migraine in France: prevalence and clinical features in adults. GRIM.

In November 1990 a nationwide survey of migraine was conducted in France on a representative sample of residents aged 15 years and older. The diagnosis of migraine was based on the International Headache Society (IHS) classification. In a previous study, we validated a diagnostic algorithm which classifies headache sufferers as IHS migraine, "borderline" migraine, possible migraine and non-migrainous headache. The overall prevalence of migraine patients with the IHS criteria in the present study was 8.1%; another 4% were classified as "borderline" migraine, which we in fact considered as definite migraine. Age, gender and occupation were found to be risk factors for migraine. Neither frequency and duration of attacks nor length of time of disease differed with gender. Expressed intensity of attacks, however, was greater in females.

Adolescent↗
PubMed1992

Expert system design in hematology diagnosis.

A two-part study was designed to test the hypothesis that sufficient information is available from a modern hematology analyzer (the Coulter STKS) to reach a reliable intermediate conclusion which can be used as input to the next decision-making level in the design of a high-performance expert system for hematology diagnosis. In phase one, we analyzed the performance of three probabilistic systems (using Bayes' rule) which interpret STKS data: a control system which took the traditional approach of classifying cases into specific diagnoses, and two test systems which were designed to reach only an intermediate conclusion but not a final diagnosis. One of the test systems classified cases into "textbook categories" of disease and the other utilized defined diagnostic patterns. The systems were tested with 150 cases. The pattern approach ranked the correct choice first in 141 of 150 cases (94%). In phase two, we abandoned Bayes' rule, reformulated the pattern approach into a heuristic classification system, and tested its reliability on 820 cases. The algorithm of the reformulated system was able to classify all 820 cases into the same predominant pattern as a panel of three experienced laboratory hematologists.

Algorithms↗
PubMed1992

Case-mix groups for VA hospital-based home care.

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

Aftercare↗
PubMed1991

[Plastic surgery measures in combined soft tissue defects of the lower leg].

Isolated or combined soft tissue defects of the lower leg are still a challenge as regard diagnostic and differential treatment. We distinguish acute and chronic isolated or combined soft tissue defect. Acute trauma within a multiple injury pattern is a special form of the acute soft tissue defect. For diagnostic purposes we use a preoperative and intraoperative standardized diagnostic programme. A new classification oriented towards the therapeutic procedure is presented, as is therapy algorithm which has been created.

Follow-Up Studies↗
PubMed1992

Multiple dipole modeling and localization from spatio-temporal MEG data.

An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in response to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large number of neurons located in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experiments is the localization of these neural current dipoles. We present here a linear algebraic framework for three common spatio-temporal dipole models: i) unconstrained dipoles, ii) dipoles with a fixed location, and iii) dipoles with a fixed orientation and location. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. With a common model, we show how the parameter estimation problem may be decomposed into the estimation of the time invariant parameters using nonlinear least-squares minimization, followed by linear estimation of the associated time varying parameters. A subspace formulation is presented and used to derive a suboptimal least-squares subspace scanning method. The resulting algorithm is a special case of the well-known MUltiple SIgnal Classification (MUSIC) method, in which the solution (multiple dipole locations) is found by scanning potential locations using a simple one dipole model. Principal components analysis (PCA) dipole fitting has also been used to individually fit single dipoles in a multiple dipole problem. Analysis is presented here to show why PCA dipole fitting will fail in general, whereas the subspace method presented here will generally succeed. Numerically efficient means of calculating the cost functions are presented, and problems of model order selection and missing moments are discussed. Results from a simulation and a somatosensory experiment are presented.

Algorithms↗
PubMed1977

[Interpretation of pulse curves by means of the Walsh analysis].

A method for classification of medical data using the Walsh transformation is demonstrated. The Walsh spectrum was obtained by the algorithm of Andrews-Kane-Pratt. The spectral points were used to declare the signals normal or abnormal. The example described in this paper shows that in the case of pulse wave the classification is successful in 93 percent.

Carotid Arteries↗
PubMed1992

Generation of a substructure library for the description and classification of protein secondary structure. I. Overview of the methods and results.

Protein secondary structure has been typically classified into four major classes--alpha-helices, extended strands, reverse turns, and loops. Available methods for secondary structure analysis utilize predefined structure templates to search for structural matches among proteins. By this approach a significant portion of a proteins backbone conformation is assigned to one of a limited number of conformations or, if unassigned, to random coil. To expand our ability to describe protein secondary structure, we have developed an algorithm that operates independently of a predefined structure template. The procedure uses two geometric descriptors, the linear distance and the backbone dihedral angle, to represent the conformation form the alpha-carbon coordinates. The algorithm functions by searching for conformationally equivalent, contiguous fragments without regard to secondary structural classification and is thus independent of the complexity of the backbone fold. The result is a library of conformationally equivalent structure fragments that exhibit some novel characteristics. The library contains features that reproduce the major secondary structure classes as well as defining conformations previously described only as random or undefined conformations. Additionally, the library defines several subclassifications of beta-strands. We present here a validation of this method and a presentation and discussion of the most significant results. In a second study, we report the results of application of this method to spectra-structure correlations in Fourier transform infrared spectroscopy.

Algorithms↗
PubMed1992

Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

Algorithms↗