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Results for “Classification Algorithms”

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At least 127 records · Page 7Linked to original sources

PubMed1990

Electrocardiographic tall R waves in the right precordial leads. Comparison of recently proposed ECG and VCG criteria for distinguishing posterolateral myocardial infarction from prominent anterior forces in normal subjects.

Electrocardiographic tall R waves in the right precordial leads may be present in patients with posterior myocardial infarction, right ventricular hypertrophy, various conduction disturbances, and some forms of cardiomyopathy and in clinically otherwise normal subjects with prominent anterior electromotive forces. Clinical uncertainty most often arises in distinguishing possible prior posterolateral myocardial infarction (PMI) from the unusual normal variant (PAF). The ECGs and VCGs of 15 subjects with posterolateral infarction were compared with tracings from 12 subjects with no evidence of cardiac disease, all individuals demonstrating tall R waves (R/S greater than 1.0 in V1 and/or V2) in the right precordial leads on surface ECG. By standard ECG, the infarction group was characterized by taller T waves in leads V1 and V2, shorter T waves in V6, greater T2-T6 index, and a more negative two variable function as described by Nestico. By VCG, the infarction group was characterized by a more anteriorly oriented T loop, more leftward maximal frontal plane QRS vector and a lower calculated -45 degrees/ab, as described by Suzuki. An algorithm was proposed that permitted proper classification (PAF vs. PMI) based on ECG criteria in 75% of subjects with 90% accuracy. This compared favorably with performance of the Frank vectorcardiogram, including using more recently proposed criteria. Routine use of the VCG, therefore, in this clinical setting may no longer be justified.

Diagnosis, Differential↗

PubMed1988

Unsupervised waveform classification for multi-neuron recordings: a real-time, software-based system. I. Algorithms and implementation.

We describe a new, mostly software-based device for the sorting of waveforms in an extracellular multi-neuron recording situation. The sorting algorithm is largely unattended, and, after an initial 'learning' process, works in real time. Shape comparisons are based on up to 8 time points in the waveform; these points (the reduced feature set) are chosen automatically by analyzing the current incoming data stream. A feasibility version has been implemented on a LSI-11/2 system, using FORTRAN for set-up calculations and assembler for the real-time operations. Detailed comparisons with performance of other sorting devices are presented in the companion paper.

Algorithms↗

PubMed1989

[Quantitative studies on the influence of connective tissue and fatty structures on the ultrasonic image of the liver].

Using a computerized ultrasound system, 63 autopsied livers were examined. A quantitative description of the ultrasound image was made by using statistical parameters from pattern recognition algorithms. As a reference a chemical/morphometrical classification into normal, diffuse and regional fatty infiltration and fibrosis/cirrhosis was performed. Diagnostic accuracy varied from 77% to 92%. Ultrasound tissue characterisation showed an influence of fatty and connective tissue structures on image parameters. This supplies evidence for the fact that computerised ultrasound examination yields more information from the ultrasound image than the normal observer. This may be the cause of an increased diagnostic accuracy when using computerized ultrasound systems, especially in the case of fibrosis/cirrhosis.

Diagnosis, Computer-Assisted↗

PubMed1991

[Rough sets theory in structure-activity relationship analysis of quaternary pyridinium compounds].

Relationship between chemical structure and antimicrobial activity of 53 quaternary pyridinium compounds is analysed using the theory of rough sets. The compounds are described by 8 attributes concerning structure and are divided into 5 classes of activity. The description builds up an information system. Using the rough sets approach a smallest set of attributes significant for a high quality of classification has been found. A decision algorithm has been derived from the information system showing important relations between structure and activity. It may be helpful in supporting decisions concerning synthesis of new antimicrobial compounds.

Algorithms↗

PubMed1991

[Semi-automatic TNM classification of malignant tumors with the ESTER system exemplified by the larynx].

Classification of tumours according to the TNM scheme has been accepted worldwide. However, vague baseline assessments and borderline cases render a comparison of the outcome on the basis of TNM classification impossible. Therefore we integrated the TNM rules as a new algorithm into an existing expert system for determining therapy. Thus, every tumour documented with the ESTHER system is automatically classified according to current TNM rules. The program is designed to cope with future changes of the TNM system: raw data are used for classification so that only the algorithms need to be modified.

Expert Systems↗

PubMed1985

The research diagnostic criteria for endogenous depression and the dexamethasone suppression test: a discriminant function analysis.

Most studies examining the validity of the Research Diagnostic Criteria (RDC) for endogenous depression have been negative. RDC endogenous subtyping is not associated with short- or long-term treatment outcome, family history of affective disorder, or premorbid personality disorder. Studies examining its relationship to the dexamethasone suppression test (DST) are mixed; half report a significant association, and half do not. The RDC endogenous diagnosis may lack validity either because the criteria do not represent, or are not specific to, the endogenous subtype, or the diagnostic algorithm is inappropriate. In the present study, we conduct a discriminant function analysis on the 10 criteria for the endogenous subtype using DST results as the independent variable. We constructed a new diagnostic algorithm and cross-validated it on a second patient sample. In both samples the discriminant function classification was significantly associated with DST results, whereas the RDC algorithm was not.

Adult↗

PubMed1986

Applications of computerized interactive morphometry in pathology. II. A model for computer generated diagnosis.

We present a model for the analysis and tentative diagnosis of pathologic problems by a trained observer utilizing data generated by a video based computerized interactive morphometry system in conjunction with multivariate methods of discriminant classificatory analysis that separate classes based on unweighted numerical values and with adhoc algorithms based on hierarchical analysis. The model was tested with two diagnostic problems that could benefit from a morphometric approach: the classification of non-Hodgkin's lymphomas from routine histologic slides and the distinction of malignant mesotheliomas from benign effusions in smears prepared from pleural effusions. The touch-sensitive screen of the computerized interactive morphometry system enables trained observers to measure the real time image of profiles of interest either by using graphic standards or simply by touching the two extreme points of the diameter of interest. This procedure generates multiple data in the form of classes that can be effectively used toward the more objective discrimination of lesions of difficult classification by analysis with a combination of adhoc diagnostic algorithms and multivariate statistical methods.

Computers↗

PubMedDate not supplied

[Assessment of the degree of severity of radiation lesions in dogs using densitometric-geometric parameters of the blood lymphocytes].

Changes in densitometric-geometric parameters of lymphocyte nuclei of dog peripheral blood detected at early times (6 and 24 H) following gamma irradiation with 2.5 and 6.0 Gy are given a composite description. Using densitometric-geometric parameters the authors have developed an algorithm allowing for a 90% accuracy in the classification of objects according to the severity of radiation affection.

Algorithms↗

PubMed1979

A national study of medical and surgical specialties. III. An empirical approach to the classification of patient care.

A major feature of a national survey of medical and surgical specialties is the development and application of an algorithm for classifying patient care services provided by physicians. The care classification reflects much of prevailing opinion regarding what constitutes primary and nonprimary care. The classification system provides a powerful tool for the analysis of patient care services, since it is based on conditions of access to care, the physician's role in providing the care, measures associated with continuity of care, and a proxy measure of comprehensiveness of care. Furthermore, it is based on the recordings by physicians of actual patient-encounter characteristics and is not operationally dependent on physician characteristics or propensities.

Cardiology↗

PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗

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↗

PubMed1990

[Large bowel cancer: prognostic factors, surgical treatments and their results].

Although the classification proposed by Dukes has been repeatedly modified, causing unnecessary confusion, his original concept remains unrefuted; cancer penetration through the bowel wall and lymph node metastasis are two major prognostic factors, of which nodal metastasis represents a more advanced stage. However, the results of our exhaustive computer analyses did not support this concept, and better classifications may be developed using our computer algorithm, enabling us to refine the indication for extensive surgery or limited resection. There are two trends of surgical treatment in Japan. One is an attempt to extend the area of resection including the paraaortic and parailiac nodes, and also iliac vessels. Patients treated by this method show a higher survival rate if compared with historical controls in Japan. However, extensive surgery of the rectum is associated with poor quality of life with bladder and anal dysfunctions as well as sexual impotence. The other trend is to limit the extent of resection and minimize the functional defect. The organs thus saved include the sphincter and autonomic nerves. The results are almost comparable with those of more radical surgery. With aggressive re-resection of recurrent tumors in the liver, lungs, lymph nodes and local areas, the number of long-term survivors is now increasing who would otherwise have died.

Colorectal Neoplasms↗

PubMed1989

Automated rule based graded analysis of ambulatory cassette EEGs.

We describe algorithms, developed on a PDP-11/73 microcomputer, which identify spikes/sharp waves (STs), spike-and-wave complexes (SSWs), artifacts and background activity in 4-channel ambulatory EEGs. The algorithms were trained using 40 database segments. Time domain/mimetic methods were used and semantic rules, based on morphology and multi-channel contextual information, were developed to mimic the principles used in visual interpretation. The likelihood of STs/SSWs being genuine was graded from 10 to 1. This approach avoids forced classification of each event as genuine ST/SSW or not. The algorithms were then evaluated using 60 independent segments. STs/SSWs graded greater than 7 had significantly higher probability (P less than 0.005) of being genuine than those graded less than or equal to 7. Less than 4% of STs/SSWs identified by both electroencephalographers were missed. None was distinct. All 113 artifacts resembling STs/WWs were graded less than or equal to 7. Classification of 969/1117 (86%) waves in the background matched that of one electroencephalographer. The algorithms can be extended to 8- or more-channel EEGs.

Algorithms↗

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↗