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Machine learning.

Machine learning is a branch of computer science aimed at enabling computers to learn new behavior based on empirical data. ... More
Salem Press Encyclopedia, 2026. 2p.

Academic Journal

An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care.

Authors : Brima Y; Computer Vision Group, Institute of Cognitive Science, Osnabrück University, D-49090, Osnabrueck, Germany. .; Atemkeng M

Subjects: Critical Care*/Critical Care*/Critical Care*/methods ; Machine Learning*; Humans

Source: Scientific reports [Sci Rep] 2026 Jun 10; Vol. 16 (1). Date of Electronic Publication: 2026 Jun 10.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

An explainable machine learning approach to predict fragility fractures and the identification of important features.

Authors : Borhan S; Department of Health Research Methods, Evidence, and Impact, McMaster University, Martha Wing, H321, 50 Charlton Avenue East, Hamilton, ON, L8N 1Y3, Canada. .; Research Methodology Centre, Research Institute of St Joseph's Healthcare Hamilton, Hamilton, ON, Canada. .

Subjects: Osteoporotic Fractures*/Osteoporotic Fractures*/Osteoporotic Fractures*/epidemiology ; Osteoporotic Fractures*/Osteoporotic Fractures*/Osteoporotic Fractures*/diagnosis ; Machine Learning*

Source: Scientific reports [Sci Rep] 2026 Jun 02; Vol. 16 (1). Date of Electronic Publication: 2026 Jun 02.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Machine Learning-Based Accurate Full-Sib Family Assignment in Sturgeon Using Whole-Genome Sequencing Data.

Authors : Yan J; Beijing Key Laboratory of Fisheries Biotechnology, Fisheries Science Institute, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100068, China.; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China.

Subjects: Fishes*/Fishes*/Fishes*/genetics ; Whole Genome Sequencing*/Whole Genome Sequencing*/Whole Genome Sequencing*/methods ; Machine Learning*

Source: International journal of molecular sciences [Int J Mol Sci] 2026 May 14; Vol. 27 (10). Date of Electronic Publication: 2026 May 14.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067

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Academic Journal

Machine Learning-Based Prediction of Performance Gaps in Rowing and Identification of Key Training Monitoring Indicators.

Authors : Li J; Division of Sports Science and Physical Education, Tsinghua University, Beijing 100084, China.; Liu G

Subjects: Athletic Performance*/Athletic Performance*/Athletic Performance*/physiology ; Athletic Performance*/Athletic Performance*/Athletic Performance*/statistics & numerical data ; Machine Learning*/Machine Learning*/Machine Learning*/statistics & numerical data

Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 May 10; Vol. 26 (10). Date of Electronic Publication: 2026 May 10.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220

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Academic Journal

An XGBoost-Based Multicenter Model for Predicting HBV-Related Hepatocellular Carcinoma: Development and Validation.

Authors : Lin Y; Department of Hepatology, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China.; Department of Hepatology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.

Subjects: Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/virology ; Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/diagnosis ; Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/Carcinoma, Hepatocellular*/mortality

Source: Cancer medicine [Cancer Med] 2026 May; Vol. 15 (5), pp. e71887.Publisher: John Wiley & Sons Ltd Country of Publication: United States NLM ID: 101595310 Publication Model: Print Cited Medium: Internet ISSN:

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Academic Journal

A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.

Authors : Sakane N; Division of Preventive Medicine, Clinical Research Institute, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.; Hirota Y

Subjects: Diabetes Mellitus, Type 1*/Diabetes Mellitus, Type 1*/Diabetes Mellitus, Type 1*/psychology ; Diabetes Mellitus, Type 1*/Diabetes Mellitus, Type 1*/Diabetes Mellitus, Type 1*/complications ; Machine Learning*

Source: Journal of diabetes investigation [J Diabetes Investig] 2026 Apr; Vol. 17 (4), pp. 689-697. Date of Electronic Publication: 2026 Feb 18.Publisher: Asian Association for the Study of Diabetes and Blackwell Pub. Asia Country of Publication: Japan NLM ID: 101520702 Publication Model:

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Academic Journal

Automated Cryo-EM and Supervised Machine Learning Enable Reproducible Characterization of Extracellular Vesicles and Co-Isolating Particles.

Authors : Enciso-Martinez A; Oncode Institute and Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands.; Biomedical Engineering & Physics

Subjects: Cryoelectron Microscopy*/Cryoelectron Microscopy*/Cryoelectron Microscopy*/methods ; Extracellular Vesicles*/Extracellular Vesicles*/Extracellular Vesicles*/ultrastructure ; Extracellular Vesicles*/Extracellular Vesicles*/Extracellular Vesicles*/metabolism

Source: Journal of extracellular vesicles [J Extracell Vesicles] 2026 Apr; Vol. 15 (4), pp. e70273.Publisher: Wiley Country of Publication: United States NLM ID: 101610479 Publication Model: Print Cited Medium: Internet ISSN: 2001-3078

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Academic Journal

Geospatial variation and machine learning approaches to predict open defecation practice in Zambia.

Authors : Birlie AA; Department of Health Informatics, College of Medicine and Health Science, Samara University, Samara, Ethiopia.; Meshesha NA

Subjects: Machine Learning* ; Defecation*; Zambia

Source: PloS one [PLoS One] 2026 Jun 10; Vol. 21 (6), pp. e0350923. Date of Electronic Publication: 2026 Jun 10 (Print Publication: 2026).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Exploration of comorbidity mechanisms between chronic pain and depression: Machine learning prediction models and SHAP interpretability analysis based on the CHARLS cohort.

Authors : Dai TM; Affiliated Hospital 6 of Nantong University, Yancheng, China.; Yancheng Third People's Hospital, the affiliated hospital of Jiangsu Medical College, Yancheng, China.

Subjects: Chronic Pain*/Chronic Pain*/Chronic Pain*/epidemiology ; Chronic Pain*/Chronic Pain*/Chronic Pain*/psychology ; Depression*/Depression*/Depression*/epidemiology

Source: PloS one [PLoS One] 2026 Jun 08; Vol. 21 (6), pp. e0349135. Date of Electronic Publication: 2026 Jun 08 (Print Publication: 2026).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Leveraging nonlinear relationships and interactions to improve 30-day pneumonia readmission machine learning models.

Authors : Mortensen EM; University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, United States of America.; Atuegwu N

Subjects: Patient Readmission*/Patient Readmission*/Patient Readmission*/statistics & numerical data ; Pneumonia*/Pneumonia*/Pneumonia*/therapy ; Pneumonia*/Pneumonia*/Pneumonia*/epidemiology

Source: PloS one [PLoS One] 2026 Jun 05; Vol. 21 (6), pp. e0349804. Date of Electronic Publication: 2026 Jun 05 (Print Publication: 2026).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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نتائج البحث

فلترة النتائج

Machine learning.

An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care.

An explainable machine learning approach to predict fragility fractures and the identification of important features.

Machine Learning-Based Accurate Full-Sib Family Assignment in Sturgeon Using Whole-Genome Sequencing Data.

Machine Learning-Based Prediction of Performance Gaps in Rowing and Identification of Key Training Monitoring Indicators.

An XGBoost-Based Multicenter Model for Predicting HBV-Related Hepatocellular Carcinoma: Development and Validation.

A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.

Automated Cryo-EM and Supervised Machine Learning Enable Reproducible Characterization of Extracellular Vesicles and Co-Isolating Particles.

Geospatial variation and machine learning approaches to predict open defecation practice in Zambia.

Exploration of comorbidity mechanisms between chronic pain and depression: Machine learning prediction models and SHAP interpretability analysis based on the CHARLS cohort.

Leveraging nonlinear relationships and interactions to improve 30-day pneumonia readmission machine learning models.

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