Performance of machine learning-based prediction models for hypoglycemia in Chinese patients with diabetes: A systematic review and meta-analysis.

Authors: Yan J; Department of General Practice, the People's Hospital of Leshan, Leshan, China., Song Y; Department of General Practice, the People's Hospital of Leshan, Leshan, China., Du Y; Department of General Practice, the People's Hospital of Leshan, Leshan, China., Li F; Department of General Practice, the People's Hospital of Leshan, Leshan, China.

Source: PloS one [PLoS One] 2026 Sep 22; Vol. 21 (9), pp. e0358876. Date of Electronic Publication: 2026 Sep 22 (Print Publication: 2026).

Publication Type: Journal Article; Systematic Review; Meta-Analysis

Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

MeSH Terms: Hypoglycemia*/diagnosis , Hypoglycemia*/epidemiology , Boosting Machine Learning Algorithms* , Diabetes Mellitus*, China/epidemiology ; Humans ; Classification Algorithms ; Predictive Learning Models ; Random Forest ; ROC Curve ; East Asian People

A Hierarchical Machine Learning-Based Framework for Clinical Decision Support in Foot Orthosis Prescription: Algorithm Development and Validation Study.

Authors: Jung JY; Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeonbuk-do, 54896, Republic of Korea, 82 63-270-4063, 82 63-270-2247., Yang W; Cyber Warfare Technology Research Center, Cyber Security Research Division, Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea., Kim JJ; Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeonbuk-do, 54896, Republic of Korea, 82 63-270-4063, 82 63-270-2247.; Research Center of Healthcare & Welfare Instrument for the Aged, Jeonbuk National University, Jeonju, Jeonbuk-do, Republic of Korea.

Source: JMIR medical informatics [JMIR Med Inform] 2026 Sep 22; Vol. 14, pp. e92831. Date of Electronic Publication: 2026 Sep 22.

Publication Type: Journal Article; Observational Study; Validation Study

Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101645109 Publication Model: Electronic Cited Medium: Internet ISSN: 2291-9694 (Electronic) Linking ISSN: 22919694 NLM ISO Abbreviation: JMIR Med Inform Subsets: MEDLINE

MeSH Terms: Foot Orthoses*/statistics & numerical data , Foot Orthoses*/standards , Prescriptions*/statistics & numerical data , Prescriptions*/standards , Boosting Machine Learning Algorithms* , Decision Support Systems, Clinical*, Female ; Humans ; Male ; Middle Aged ; Classification Algorithms ; Predictive Learning Models ; Retrospective Studies

Validation of a machine learning model for predicting early deterioration in the emergency department.

Authors: Lee YR; Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada., Ruffolo I; Department of Computer Science, University of Toronto, Toronto, ON, Canada., Mashouri P; Department of Computer Science, University of Toronto, Toronto, ON, Canada., Brudno M; Department of Computer Science, University of Toronto, Toronto, ON, Canada., Ben-Yakov M; Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Department of Medicine, University of Toronto, Toronto, ON, Canada; Department of Emergency Medicine, University of Toronto, Toronto, ON, Canada. Electronic address: maxim.benyakov@utoronto.ca.

Source: The American journal of emergency medicine [Am J Emerg Med] 2026 Sep; Vol. 107, pp. 77-82. Date of Electronic Publication: 2026 May 07.

Publication Type: Journal Article; Validation Study

Journal Info: Publisher: W B Saunders Country of Publication: United States NLM ID: 8309942 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8171 (Electronic) Linking ISSN: 07356757 NLM ISO Abbreviation: Am J Emerg Med Subsets: MEDLINE

MeSH Terms: Triage*/methods , Boosting Machine Learning Algorithms* , Clinical Deterioration* , Emergency Service, Hospital*, Aged ; Female ; Humans ; Classification Algorithms ; Early Warning Score ; Predictive Learning Models ; Risk Assessment

Growth Hormone Treatment Response and Machine Learning-Based Prediction in Idiopathic GHD and ISS: Analysis of the Korean LG Growth Study.

Authors: Park J; Department of Pediatrics, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea., Joo EY; Department of Pediatrics, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea., Kim SJ; Department of Pediatrics, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea., Yoo MJ; Department of Pediatrics, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea., Lee JE; Department of Pediatrics, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea.

Source: Clinical endocrinology [Clin Endocrinol (Oxf)] 2026 Sep; Vol. 105 (3), pp. 322-330. Date of Electronic Publication: 2026 May 17.

Publication Type: Journal Article; Observational Study

Journal Info: Publisher: Blackwell Publishing Country of Publication: England NLM ID: 0346653 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2265 (Electronic) Linking ISSN: 03000664 NLM ISO Abbreviation: Clin Endocrinol (Oxf) Subsets: MEDLINE

MeSH Terms: Dwarfism, Pituitary*/drug therapy , Growth Disorders*/drug therapy , Human Growth Hormone*/therapeutic use , Human Growth Hormone*/deficiency , Boosting Machine Learning Algorithms*, Body Height/drug effects ; Adolescent ; Child ; Female ; Humans ; Male ; Classification Algorithms ; Predictive Learning Models ; Republic of Korea ; Retrospective Studies ; Treatment Outcome

Machine-learning-based estimation of ground-level NO2 concentrations across Southeastern Europe using multi-source satellite, reanalysis, and emission data.

Authors: Bilgiç E; Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye. Electronic address: efem.bilgic@deu.edu.tr., Elbir T; Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Türkiye; Environmental Research and Application Center (CEVMER), Dokuz Eylul University, Izmir, Türkiye.

Source: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2026 Aug 15; Vol. 403, pp. 128461. Date of Electronic Publication: 2026 May 28.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8804476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6424 (Electronic) Linking ISSN: 02697491 NLM ISO Abbreviation: Environ Pollut Subsets: MEDLINE

MeSH Terms: Air Pollutants*/analysis , Environmental Monitoring*/methods , Nitrogen Dioxide*/analysis , Boosting Machine Learning Algorithms*, Air Pollution/statistics & numerical data ; Europe ; Satellite Imagery

Microplastics deplete soil available nutrients: a global meta-analysis with machine learning reveals critical thresholds and interactive controls.

Authors: Xiang Y; School of Geography and Resources, Guizhou Education University, Guiyang 550018, China., Peñuelas J; CSIC Global Ecology Unit, CREAF-CSIC-UAB, 08193 Bellaterra, Catalonia, Spain; CREAF - Ecological and Forestry Applications Research Centre, 08193 Cerdanyola del Vallès, Catalonia, Spain., Rillig MC; Institut für Biologie, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Freie Universität Berlin, Berlin D-14195, Germany., Luo X; School of Geography and Resources, Guizhou Education University, Guiyang 550018, China., Nizzetto L; Norwegian Institute for Water Research, Økernveien 94, 0579 Oslo, Norway., Akkanen J; Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, Finland., Liu Y; School of Biological Sciences, Guizhou Education University, Guiyang 550018, China., Luo Y; School of Geography and Resources, Guizhou Education University, Guiyang 550018, China., Yao B; State Key Laboratory of Tree Genetics and Breeding, Institute of Ecology Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China. Electronic address: acmn21@caf.ac.cn., Li Y; Grasslands and Sustainable Farming, Production Systems Unit, Natural Resources Institute Finland, Halolantie 31 A, 71750, Maaninka, Finland. Electronic address: yuan.li@luke.fi.

Source: Water research [Water Res] 2026 Aug 15; Vol. 301, pp. 126056. Date of Electronic Publication: 2026 May 02.

Publication Type: Journal Article; Meta-Analysis

Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE

MeSH Terms: Soil*/chemistry , Boosting Machine Learning Algorithms* , Microplastics*, Nitrogen ; Nutrients ; Phosphorus ; Soil Pollutants

A scalable Earth observation-based machine learning framework for high-resolution mapping and uncertainty assessment of climate-sensitive child health vulnerability in Bangladesh.

Authors: Moniruzzaman M; Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh. Electronic address: mzges@ru.ac.bd.

Source: The Science of the total environment [Sci Total Environ] 2026 Aug 01; Vol. 1042, pp. 181919. Date of Electronic Publication: 2026 Jun 01.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: MEDLINE

MeSH Terms: Child Health*/statistics & numerical data , Boosting Machine Learning Algorithms* , Climate Change*, Environmental Monitoring/methods ; Humans ; Bangladesh ; Climate ; Predictive Learning Models ; Random Forest ; Uncertainty

Development of a LightGBM-based survival prediction model for major burn patients using LDH/LYM ratio as the core predictor.

Authors: Sun N; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China., Fan H; Department of Burn and Plastic Surgery, No.971 Hospital of the People's Liberation Army Navy, Qingdao, PR China., Zhu L; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China., Yi W; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China., Zhang L; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China., Wang G; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China. Electronic address: 13023225660@163.com., Wu G; Department of Burn Surgery, Changhai Hospital, Navy Medical University, Shanghai 200433, PR China. Electronic address: 18019359841@163.com.

Source: Burns : journal of the International Society for Burn Injuries [Burns] 2026 Aug; Vol. 52 (6), pp. 108022. Date of Electronic Publication: 2026 Apr 17.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 8913178 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1409 (Electronic) Linking ISSN: 03054179 NLM ISO Abbreviation: Burns Subsets: MEDLINE

MeSH Terms: Burns*/mortality , Burns*/blood , L-Lactate Dehydrogenase*/blood , Boosting Machine Learning Algorithms*, Biomarkers/blood ; Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Area Under Curve ; Body Surface Area ; Kaplan-Meier Estimate ; Lymphocyte Count ; Multivariate Analysis ; Prognosis ; Retrospective Studies ; ROC Curve ; Prediction Methods, Machine

An ensemble machine learning approach for predicting anemia among under-five children in malaria-endemic sub-Saharan African countries.

Authors: Tekeba B; Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. berishboss7@gmail.com., Baykemagn ND; Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia., Mengesha AT; Department of Information Science, College of Informatics, University of Gondar, Gondar, Ethiopia., Mengstie MA; Department of Information Science, College of Informatics, University of Gondar, Gondar, Ethiopia.

Source: Infectious diseases of poverty [Infect Dis Poverty] 2026 Jul 13; Vol. 15 (1). Date of Electronic Publication: 2026 Jul 13.

Publication Type: Journal Article

Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101606645 Publication Model: Electronic Cited Medium: Internet ISSN: 2049-9957 (Electronic) Linking ISSN: 20499957 NLM ISO Abbreviation: Infect Dis Poverty Subsets: MEDLINE

MeSH Terms: Anemia*/epidemiology , Anemia*/diagnosis , Anemia*/etiology , Malaria*/epidemiology , Malaria*/complications , Boosting Machine Learning Algorithms*, Africa South of the Sahara/epidemiology ; Child, Preschool ; Female ; Humans ; Infant ; Male ; Classification Algorithms ; Cross-Sectional Studies ; Predictive Learning Models ; Random Forest

Development and Validation of an Interpretable Machine Learning Model for Predicting 1-Year Cardiac Death After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: A Multicenter Study.

Authors: Liu H; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China.; Department of Cardiology Heart Center, Inner Mongolia People's Hospital Hohhot China., Hu S; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Zhang Y; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Gu T; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Zhang J; Cardiovascular Research Institute University of California San Francisco CA USA., Wu X; Institute for Global Health Sciences University of California San Francisco CA USA., Liu X; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Shao X; NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology Tianjin Medical University Tianjin China., Wang L; Cardiology Department, Chest Hospital Tianjin University Tianjin China., Tse G; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China.; School of Nursing and Health Studies Hong Kong Metropolitan University Hong Kong China., Liu T; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China., Chen K; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology The Second Hospital of Tianjin Medical University Tianjin China.

Source: Journal of the American Heart Association [J Am Heart Assoc] 2026 Jul 07; Vol. 15 (13), pp. e044796. Date of Electronic Publication: 2026 Jun 23.

Publication Type: Journal Article; Multicenter Study; Validation Study; Research Support, Non-U.S. Gov't

Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101580524 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-9980 (Electronic) Linking ISSN: 20479980 NLM ISO Abbreviation: J Am Heart Assoc Subsets: MEDLINE

MeSH Terms: Myocardial Infarction*/mortality , Myocardial Infarction*/surgery , Percutaneous Coronary Intervention*/adverse effects , Percutaneous Coronary Intervention*/mortality , Boosting Machine Learning Algorithms*, China/epidemiology ; Risk Assessment/methods ; Aged ; Female ; Humans ; Male ; Middle Aged ; Predictive Learning Models ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Risk Factors ; Time Factors

Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.

Authors: Munroe HN; Division of Plant Breeding Methodology, Department of Crop Sciences, University of Göttingen, Carl-Sprengel-Weg 1, Göttingen 37075, Lower Saxony, Germany., Osatohanmwen BE; Division of Plant Breeding Methodology, Department of Crop Sciences, University of Göttingen, Carl-Sprengel-Weg 1, Göttingen 37075, Lower Saxony, Germany.; Center for Integrated Breeding Research, University of Göttingen, Albrecht-Thaer-Weg 3, Göttingen 37075, Lower Saxony, Germany., Reza Sharifi A; Center for Integrated Breeding Research, University of Göttingen, Albrecht-Thaer-Weg 3, Göttingen 37075, Lower Saxony, Germany.; Division of Animal Breeding and Genetics, Department of Animal Sciences, University of Göttingen, Albrecht-Thaer-Weg 3, Göttingen 37075, Lower Saxony, Germany.

Source: G3 (Bethesda, Md.) [G3 (Bethesda)] 2026 Jul 06; Vol. 16 (7).

Publication Type: Journal Article

Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Print Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE

MeSH Terms: Triticum*/genetics , Genomics*/methods , Selection, Genetic* , Plant Breeding* , Boosting Machine Learning Algorithms*, Genome, Plant ; Hybridization, Genetic

PRIMARCH-APP AI: Development and Internal Validation of an Explainable Machine Learning Model for Preoperative Prediction of Complicated Acute Appendicitis.

Authors: Molnar DC, Cosma CD, Botoncea M, Tudor A, Butiurcă VO, Molnar C, Andrei BS

Source: Chirurgia (Bucharest, Romania : 1990) [Chirurgia (Bucur)] 2026 Jul; Vol. 121 (4), pp. 371-382.

Publication Type: Journal Article; Observational Study; Validation Study

Journal Info: Publisher: Editura Celsius Country of Publication: Romania NLM ID: 9213031 Publication Model: Print Cited Medium: Print ISSN: 1221-9118 (Print) Linking ISSN: 12219118 NLM ISO Abbreviation: Chirurgia (Bucur) Subsets: MEDLINE

MeSH Terms: Appendectomy*/methods , Appendicitis*/surgery , Appendicitis*/diagnosis , Appendicitis*/complications , Boosting Machine Learning Algorithms*, Adult ; Female ; Humans ; Male ; Middle Aged ; Acute Disease ; Logistic Models ; Predictive Learning Models ; Predictive Value of Tests ; Preoperative Period ; Prospective Studies ; Random Forest ; Reproducibility of Results ; Risk Assessment ; ROC Curve ; Sensitivity and Specificity

Machine Learning Model for Predicting Visual Acuity Improvement After Intrastromal Corneal Ring Surgery in Patients With Keratoconus.

Authors: Perez E; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Louissi N; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Kallel S; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Hays Q; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Bouheraoua N; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Hamrani M; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and., Chessel A; Laboratory for Optics and Biosciences (LOB), École Polytechnique, CNRS, Inserm, Institut Polytechnique de Paris, Palaiseau, France ., Borderie V; GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and.

Source: Cornea [Cornea] 2026 Jul 01; Vol. 45 (7), pp. 840-850. Date of Electronic Publication: 2025 Jul 23.

Publication Type: Journal Article

Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8216186 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-4798 (Electronic) Linking ISSN: 02773740 NLM ISO Abbreviation: Cornea Subsets: MEDLINE

MeSH Terms: Corneal Stroma*/surgery , Keratoconus*/surgery , Keratoconus*/physiopathology , Visual Acuity*/physiology , Boosting Machine Learning Algorithms* , Prostheses and Implants* , Prosthesis Implantation*, Refraction, Ocular/physiology ; Adolescent ; Adult ; Female ; Humans ; Male ; Young Adult ; Corneal Topography ; Predictive Learning Models ; Retrospective Studies

Key predictive factors of breast cancer based on race using machine learning models.

Authors: Yin S; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA., Nanda G; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA., Sundararajan R; School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA. Electronic address: raji@purdue.edu.

Source: Annals of epidemiology [Ann Epidemiol] 2026 Jul; Vol. 119, pp. 110080. Date of Electronic Publication: 2026 Mar 28.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 9100013 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2585 (Electronic) Linking ISSN: 10472797 NLM ISO Abbreviation: Ann Epidemiol Subsets: MEDLINE

MeSH Terms: Breast Neoplasms*/ethnology , Breast Neoplasms*/epidemiology , Racial Groups*/statistics & numerical data , Boosting Machine Learning Algorithms*, Mammography/statistics & numerical data ; Adult ; Aged ; Female ; Humans ; Middle Aged ; Young Adult ; Age Factors ; Bayes Theorem ; Classification Algorithms ; Incidence ; Logistic Models ; Prediction Algorithms ; Predictive Learning Models ; Risk Factors ; White

Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention-behaviour gap.

Authors: Wang Y; College of Economics and Management, Nanjing Forestry University, Nanjing, China.

Source: Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA [Waste Manag Res] 2026 Jul; Vol. 44 (7), pp. 1037-1060. Date of Electronic Publication: 2026 Feb 22.

Publication Type: Journal Article

Journal Info: Publisher: Sage Publications Country of Publication: England NLM ID: 9881064 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-3669 (Electronic) NLM ISO Abbreviation: Waste Manag Res Subsets: MEDLINE

MeSH Terms: Boosting Machine Learning Algorithms* , Consumer Behavior* , Food Loss and Waste* , Intention*, Humans ; China ; Predictive Learning Models ; Random Forest ; East Asian People

Predicting positive youth development among Chinese adolescents: A machine learning approach using multiwave longitudinal data.

Authors: Liu Z; Institute of Developmental Psychology, Beijing Normal University, Beijing, China., Lu Z; Institute of Developmental Psychology, Beijing Normal University, Beijing, China.; Department of Sociology, McGill University, Montréal, Canada., Wang Y; Department of Psychology, Hangzhou Normal University, Hangzhou, China., Lin D; Institute of Developmental Psychology, Beijing Normal University, Beijing, China.

Source: Applied psychology. Health and well-being [Appl Psychol Health Well Being] 2026 Oct; Vol. 18 (5), pp. e70211.

Publication Type: Journal Article

Journal Info: Publisher: Blackwell Country of Publication: England NLM ID: 101502957 Publication Model: Print Cited Medium: Internet ISSN: 1758-0854 (Electronic) Linking ISSN: 17580854 NLM ISO Abbreviation: Appl Psychol Health Well Being Subsets: MEDLINE

MeSH Terms: Depression*/psychology , Loneliness*/psychology , Adolescent Development* , Boosting Machine Learning Algorithms* , Prediction Algorithms*, Adolescent ; Child ; Female ; Humans ; Male ; China ; Longitudinal Studies ; East Asian People

Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.

Authors: Martínez-Blanco P; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain., Suárez M; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain., Mateo J; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain. Jorge.Mateo@uclm.es.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain. Jorge.Mateo@uclm.es., Gil-Rojas S; Gastroenterology Department, Cuenca University Hospital, 16002, Cuenca, Spain.; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain., Martínez-García N; Internal Medicine Unit, Guadalajara University Hospital, 19002, Guadalajara, Spain., Blasco P; Department of Pharmacy, General University Hospital, 46014, Valencia, Spain., Torralba M; Internal Medicine Unit, Guadalajara University Hospital, 19002, Guadalajara, Spain.; Faculty of Medicine, Universidad de Alcalá de Henares, 28801, Alcalá de Henares, Spain.; Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain., Torres AM; Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071, Cuenca, Spain.; Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071, Toledo, Spain.

Source: Digestive diseases and sciences [Dig Dis Sci] 2026 Oct; Vol. 71 (10), pp. 4743-4755. Date of Electronic Publication: 2026 May 30.

Publication Type: Journal Article; Comparative Study; Multicenter Study

Journal Info: Publisher: Springer Science + Business Media Country of Publication: United States NLM ID: 7902782 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-2568 (Electronic) Linking ISSN: 01632116 NLM ISO Abbreviation: Dig Dis Sci Subsets: MEDLINE

MeSH Terms: Carcinoma, Hepatocellular*/diagnosis , Carcinoma, Hepatocellular*/mortality , Carcinoma, Hepatocellular*/pathology , Liver Neoplasms*/diagnosis , Liver Neoplasms*/mortality , Liver Neoplasms*/pathology , Boosting Machine Learning Algorithms*, Aged ; Female ; Humans ; Male ; Middle Aged ; Neoplasm Staging ; Predictive Learning Models ; Prognosis ; Retrospective Studies ; Severity of Illness Index

Machine learning-driven ultrasound echo feature analysis for accurate classification and area prediction of HIFU-induced lesions: ex vivo study.

Authors: Gong G; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Liu T; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Xing B; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Ma X; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Wu M; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Li Y; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Mpehle RCM; Department of Obstetrics and Gynaecology, Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa; Department of Obstetrics and Gynaecology, Faculty of Health Science, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa., Zhou Y; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China; National Medical Products Administration (NMPA) Key Laboratory for Quality Evaluation of Ultrasonic Surgical Equipment, 507 Gaoxin Ave., Donghu New Technology Development Zone, Wuhan, Hubei 430075, China; National Engineering Research Center of Ultrasound Medicine, Chongqing 401120, China. Electronic address: yufeng.zhou@cqmu.edu.cn.

Source: Ultrasonics [Ultrasonics] 2026 Oct; Vol. 166, pp. 108112. Date of Electronic Publication: 2026 May 12.

Publication Type: Journal Article

Journal Info: Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 0050452 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1874-9968 (Electronic) Linking ISSN: 0041624X NLM ISO Abbreviation: Ultrasonics Subsets: MEDLINE

MeSH Terms: High-Intensity Focused Ultrasound Ablation*/methods , Liver*/diagnostic imaging , Liver*/surgery , Liver*/pathology , Boosting Machine Learning Algorithms*, Ultrasonography/methods ; Animals ; Cattle ; Classification Algorithms ; Prediction Algorithms ; Predictive Learning Models ; Random Forest ; Support Vector Machine

Identifying policy-relevant traffic crash risk factors in Cheongju, South Korea using logistic regression and explainable machine learning.

Authors: Lee EJ; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea., Kim S; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea.; The Korea Transport Institute, Sejong-si, Sejong, Republic of Korea., Lee HJ; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea., Jhong JH; Department of Statistics, Chung Buk National University, Cheongju, Chungbuk, Republic of Korea.

Source: PloS one [PLoS One] 2026 Jun 22; Vol. 21 (6), pp. e0350616. Date of Electronic Publication: 2026 Jun 22 (Print Publication: 2026).

Publication Type: Journal Article

Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

MeSH Terms: Accidents, Traffic*/statistics & numerical data , Accidents, Traffic*/prevention & control , Boosting Machine Learning Algorithms*, Automobile Driving/legislation & jurisprudence ; Humans ; Logistic Models ; Predictive Learning Models ; Random Forest ; Republic of Korea ; Risk Factors

Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.

Authors: Zhu Y; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Wang Y; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Zhang S; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Yang J; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Zhang F; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Liu Y; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Shang J; Department of Nephrology, The Second Hospital of Anhui Medical University, Hefei, Anhui, China., Zhang Y; Department of Health Management Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China., Wang J; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China., Liu L; Department of Hepatobiliary Surgery, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, No.96 Jinzhai Road, Hefei, Anhui, 230001, China, 86 13845159888.; Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, Hefei, Anhui, China.; Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Hefei, Anhui, China.

Source: Journal of medical Internet research [J Med Internet Res] 2026 Jun 19; Vol. 28, pp. e82529. Date of Electronic Publication: 2026 Jun 19.

Publication Type: Journal Article; Validation Study

Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE

MeSH Terms: Fatty Liver*/diagnosis , Fatty Liver*/pathology , Boosting Machine Learning Algorithms*, Adult ; Female ; Humans ; Male ; Middle Aged ; Predictive Learning Models ; Random Forest ; Severity of Illness Index ; Support Vector Machine ; Multilayer Perceptrons ; Bayes Theorem