Sustainable visions: unsupervised machine learning insights on global development goals.

Autors: García-Rodríguez A; Instituto de Física, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.; Department of Computer Science, Aalto University School of Science, Espoo, Finland.; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México., Núñez M; Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.; Instituto de Investigaciones en Biodiversidad y Medioambiente, Universidad Nacional del Comahue, Bariloche, Argentina., Pérez MR; Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco, Morelos, México., Govezensky T; Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México., Barrio RA; Instituto de Física, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México., Gershenson C; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.; Department of Systems Science and Industrial Engineering Thomas J. Watson College of Engineering and Applied Science, State University of New York at Binghamton, Binghamton, New York, United States of America.; Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.; Lakeside Labs GmbH, Klagenfurt am Wörthersee, Austria., Kaski KK; Department of Computer Science, Aalto University School of Science, Espoo, Finland., Tagüeña J; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.; Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Temixco, Morelos, México.

Font: PloS one [PLoS One] 2025 Mar 12; Vol. 20 (3), pp. e0317412. Date of Electronic Publication: 2025 Mar 12 (Print Publication: 2025).

Tipologia de la publicació: Journal Article

Informació de la publicació: 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

Terms MeSH: Sustainable Development*/trends , Unsupervised Machine Learning* , Goals*, Humans ; United Nations ; Socioeconomic Factors ; Machine Learning ; Conservation of Natural Resources/methods

Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.

Autors: Hu Y; Department of Cardiology, National Clinical Research Center for Interventional Medicine, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.; Shanghai Engineering Research Center of AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China., Yan H; Department of General Surgery, Center for Bariatric and Hernia Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, China., Liu M; Shanghai Engineering Research Center of AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.; Department of Health Management Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China., Gao J; Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China., Xie L; Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China., Zhang C; Department of Cardiology, National Clinical Research Center for Interventional Medicine, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China., Wei L; Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China., Ding Y; Department of Epidemiology, School of Public Health, and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, 200032, China. dingyy@fudan.edu.cn., Jiang H; Department of Cardiology, National Clinical Research Center for Interventional Medicine, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. jianghong_@fudan.edu.cn.; Shanghai Engineering Research Center of AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. jianghong_@fudan.edu.cn.

Font: BMC medical research methodology [BMC Med Res Methodol] 2024 Dec 19; Vol. 24 (1), pp. 309. Date of Electronic Publication: 2024 Dec 19.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288 (Electronic) Linking ISSN: 14712288 NLM ISO Abbreviation: BMC Med Res Methodol Subsets: MEDLINE

Terms MeSH: Electronic Health Records*/statistics & numerical data , Cardiovascular Diseases*/diagnosis , Unsupervised Machine Learning* , Bayes Theorem*, Humans ; Cluster Analysis ; Middle Aged ; Female ; Male ; Adult ; China/epidemiology ; Aged ; Machine Learning

Nutritional intake of micronutrient and macronutrient and type 2 diabetes: machine learning schemes.

Autors: Rashidmayvan M; Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran., Mansoori A; Department of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran., Derakhshan-Nezhad E; Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran.; Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran., Tanbakuchi D; Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran., Sangin F; Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information, Processing Ferdowsi University of Mashhad, Mashhad, Iran., Mohammadi-Bajgiran M; International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran., Abedsaeidi M; Department of Basic Sciences, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran., Ghazizadeh S; Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran., Sarabi MMT; Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran., Rezaee A; Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran.; Student Research Committee, Mazandaran University of Medical Sciences, Sari, Iran., Ferns G; Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK., Esmaily H; Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. esmailyh@mums.ac.ir.; Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. esmailyh@mums.ac.ir., Ghayour-Mobarhan M; International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran. ghayourmobarhan@yahoo.com.

Font: Journal of health, population, and nutrition [J Health Popul Nutr] 2025 Feb 07; Vol. 44 (1), pp. 31. Date of Electronic Publication: 2025 Feb 07.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: Bangladesh NLM ID: 100959228 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-1315 (Electronic) Linking ISSN: 16060997 NLM ISO Abbreviation: J Health Popul Nutr Subsets: MEDLINE

Terms MeSH: Diabetes Mellitus, Type 2*/epidemiology , Micronutrients*/administration & dosage , Machine Learning* , Nutrients*/administration & dosage , Nutrients*/analysis, Humans ; Middle Aged ; Male ; Female ; Adult ; Aged ; Incidence ; Iran/epidemiology ; Logistic Models ; Cohort Studies ; Decision Trees ; Diet/statistics & numerical data ; Supervised Machine Learning

A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment.

Autors: Yu T; School of Mathematics, Harbin Institute of Technology, Harbin, China.; Department of Mathematics, Southern University of Science and Technology, Shenzhen, China., Huang W; College of Business, Southern University of Science and Technology, Shenzhen, China.; National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen, China., Tang X; College of Business, Southern University of Science and Technology, Shenzhen, China.; National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen, China., Zheng D; College of Business, Southern University of Science and Technology, Shenzhen, China.; National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen, China.

Font: PloS one [PLoS One] 2025 Jan 10; Vol. 20 (1), pp. e0316557. Date of Electronic Publication: 2025 Jan 10 (Print Publication: 2025).

Tipologia de la publicació: Journal Article

Informació de la publicació: 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

Terms MeSH: Support Vector Machine* , Unsupervised Machine Learning* , Algorithms*, Risk Assessment/methods ; Cluster Analysis ; Humans ; Supervised Machine Learning

Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.

Autors: Stavropoulos V; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Prokofieva M; 2Victoria University, Australia., Zarate D; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Colder Carras M; 3Bloomberg School of Public Health, John Hopkins University, Baltimore, USA., Ratan R; 4Michigan State University, USA., Kowert R; 5Psychology Research Institute, Faculty of Social Studies, Masaryk University, Brno, Czech Republic.; 6Department of Communication, University of Münster, Münster, Germany., Schivinski B; 7School of Media and Communication, RMIT University, Australia., Burleigh TL; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Poulus D; 8Physical Activity, Sport and Exercise Research Theme, Faculty of Health, Southern Cross University, Gold Coast, Australia.; 9Manna Institute, Southern Cross University, Gold Coast, Australia., Karimi L; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Gorman-Alesi A; 10Catholic Care Victoria, Australia., Brown T; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Gomez R; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Hein K; 1Department of Psychology, Applied Health, School of Health and Biomedical Sciences, RMIT University, Australia., Arachchilage N; 11School of Computing, RMIT University, Australia., Griffiths MD; 12International Gaming Research Unit, Psychology Dept, Nottingham Trent University, UK.

Font: Journal of behavioral addictions [J Behav Addict] 2024 Nov 22; Vol. 13 (4), pp. 894-900. Date of Electronic Publication: 2024 Nov 22 (Print Publication: 2024).

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: Akadémiai Kiado Country of Publication: Hungary NLM ID: 101602037 Publication Model: Electronic-Print Cited Medium: Internet ISSN: 2063-5303 (Electronic) Linking ISSN: 20625871 NLM ISO Abbreviation: J Behav Addict Subsets: MEDLINE

Terms MeSH: Internet Addiction Disorder* , Machine Learning*, Humans ; User-Computer Interface ; Supervised Machine Learning ; Video Games ; Avatar

Anomaly-based threat detection in smart health using machine learning.

Autors: Tabassum M; Department of Computer Science, Bahria University, Islamabad, Pakistan., Mahmood S; Department of Computer Science, Bahria University, Islamabad, Pakistan. smahmood.buic@bahria.edu.pk., Bukhari A; Department of Information Systems and Technology, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia., Alshemaimri B; Software Engineering Department, College of Computing and Information Sciences, King Saud University, Riyadh, Saudi Arabia., Daud A; Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates. alimsdb@gmail.com., Khalique F; Centre of Excellence in Artificial Intelligence COE-AI, Bahria University, Islamabad, Pakistan.

Font: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Nov 19; Vol. 24 (1), pp. 347. Date of Electronic Publication: 2024 Nov 19.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE

Terms MeSH: Machine Learning* , Electronic Health Records*, Humans ; Computer Security ; Unsupervised Machine Learning ; Support Vector Machine

Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.

Autors: Xu S; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA., Ackerman ME; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, USA. margaret.e.ackerman@dartmouth.edu.; Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA. margaret.e.ackerman@dartmouth.edu.; Thayer School of Engineering, Dartmouth College, 14 Engineering Dr., Hanover, NH, 03755, USA. margaret.e.ackerman@dartmouth.edu.

Font: BMC bioinformatics [BMC Bioinformatics] 2024 Jun 19; Vol. 25 (1), pp. 218. Date of Electronic Publication: 2024 Jun 19.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Terms MeSH: Machine Learning*, Supervised Machine Learning ; Humans ; Computational Biology/methods ; Algorithms

Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.

Autors: Mansouri K; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Taylor K; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Auerbach S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Ferguson S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Frawley R; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Hsieh JH; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Jahnke G; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Kleinstreuer N; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Mehta S; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Moreira-Filho JT; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Parham F; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Rider C; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Rooney AA; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Wang A; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA., Sutherland V; Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.

Font: Environmental health perspectives [Environ Health Perspect] 2024 Aug; Vol. 132 (8), pp. 85002. Date of Electronic Publication: 2024 Aug 06.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: National Institute of Environmental Health Sciences Country of Publication: United States NLM ID: 0330411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-9924 (Electronic) Linking ISSN: 00916765 NLM ISO Abbreviation: Environ Health Perspect Subsets: MEDLINE

Terms MeSH: Machine Learning*, Cluster Analysis ; Unsupervised Machine Learning ; Toxicology/methods ; Algorithms

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

Autors: Tutsoy O; Adana Alparslan Turkes Science and Technology University, Adana, Turkey. otutsoy@atu.edu.tr., Koç GG; Adana Alparslan Turkes Science and Technology University, Adana, Turkey.

Font: BMC bioinformatics [BMC Bioinformatics] 2024 Mar 08; Vol. 25 (1), pp. 103. Date of Electronic Publication: 2024 Mar 08.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Terms MeSH: Algorithms* , Machine Learning*, Supervised Machine Learning

Artificial Intelligence Predicts Fitzpatrick Skin Type, Pigmentation, Redness, and Wrinkle Severity From Color Photographs of the Face.

Autors: Draelos RL; Vismedica AI, LLC, Durham, North Carolina, USA., Kesty CE; St. Petersburg Skin and Laser, St. Petersburg, Florida, USA.; Kesty AI, St. Petersburg, Florida, USA., Kesty KR; St. Petersburg Skin and Laser, St. Petersburg, Florida, USA.; Kesty AI, St. Petersburg, Florida, USA.

Font: Journal of cosmetic dermatology [J Cosmet Dermatol] 2025 Apr; Vol. 24 (4), pp. e70050.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: Blackwell Science Country of Publication: England NLM ID: 101130964 Publication Model: Print Cited Medium: Internet ISSN: 1473-2165 (Electronic) Linking ISSN: 14732130 NLM ISO Abbreviation: J Cosmet Dermatol Subsets: MEDLINE

Terms MeSH: Photography* , Skin Pigmentation* , Face* , Skin Aging* , Hyperpigmentation*/diagnosis , Hyperpigmentation*/etiology , Machine Learning*, Humans ; Artificial Intelligence ; Female ; Male ; Adult ; Erythema/etiology ; Erythema/diagnosis ; Neural Networks, Computer ; Middle Aged ; Skin/pathology ; Skin/diagnostic imaging ; Severity of Illness Index

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Autors: Manjur SM; Department of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA., Diaz LRM; Department of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA., Lee IO; Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK., Skuse DH; Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK., Thompson DA; Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.; UCL Great Ormond Street Institute for Child Health, University College London, London, UK., Marmolejos-Ramos F; Centre for Change and Complexity in Learning, University of South Australia, Adelaide, Australia., Constable PA; College of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia., Posada-Quintero HF; Department of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA. hugo.posada-quintero@uconn.edu.

Font: Journal of autism and developmental disorders [J Autism Dev Disord] 2025 Apr; Vol. 55 (4), pp. 1365-1378. Date of Electronic Publication: 2024 Feb 23.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: Springer Country of Publication: United States NLM ID: 7904301 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-3432 (Electronic) Linking ISSN: 01623257 NLM ISO Abbreviation: J Autism Dev Disord Subsets: MEDLINE

Terms MeSH: Autism Spectrum Disorder*/diagnosis , Autism Spectrum Disorder*/physiopathology , Attention Deficit Disorder with Hyperactivity*/diagnosis , Attention Deficit Disorder with Hyperactivity*/physiopathology , Electroretinography*/methods , Machine Learning*, Humans ; Male ; Female ; Child ; Adolescent ; Adult ; Retina/physiopathology ; Young Adult ; Photic Stimulation/methods

Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

Autors: Wang L; Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology, Wuhan, China., Zhang P; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China., Feng Y; Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology, Wuhan, China.; School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China., Lv W; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Britton Chance Center, MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China., Min X; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China., Liu Z; School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, China., Li J; Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology, Wuhan, China. lijin@tjh.tjmu.edu.cn., Feng Z; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. fzm198822@126.com.

Font: BMC cancer [BMC Cancer] 2025 Mar 28; Vol. 25 (1), pp. 563. Date of Electronic Publication: 2025 Mar 28.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100967800 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2407 (Electronic) Linking ISSN: 14712407 NLM ISO Abbreviation: BMC Cancer Subsets: MEDLINE

Terms MeSH: Testicular Neoplasms*/diagnostic imaging , Testicular Neoplasms*/pathology , Machine Learning* , Magnetic Resonance Imaging*/methods, Humans ; Male ; Retrospective Studies ; Adult ; Middle Aged ; Algorithms ; ROC Curve ; Young Adult ; Aged ; Adolescent ; Radiomics

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

Autors: An T; Department of Cardiology, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Han H; Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Xie J; Department of Management Center, Cancer Hospital of Huanxing Chaoyang District, Beijing, China., Wang Y; Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Zhao Y; Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China., Jia H; Department of Cardiac Surgery, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. fuwai_jiahao@163.com., Wang Y; Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Pekingunion Medical College, Beijing, China. wangyf@cicams.ac.cn.

Font: BMC cancer [BMC Cancer] 2025 Mar 27; Vol. 25 (1), pp. 552. Date of Electronic Publication: 2025 Mar 27.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100967800 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2407 (Electronic) Linking ISSN: 14712407 NLM ISO Abbreviation: BMC Cancer Subsets: MEDLINE

Terms MeSH: Bayes Theorem* , Machine Learning* , Breast Neoplasms*/drug therapy, Humans ; Female ; Middle Aged ; Risk Assessment/methods ; Adult ; Risk Factors ; Central Venous Catheters/adverse effects ; Thrombosis/etiology ; Thrombosis/epidemiology ; Aged ; Catheterization, Central Venous/adverse effects ; Antineoplastic Agents/adverse effects ; Antineoplastic Agents/therapeutic use ; ROC Curve ; Retrospective Studies ; Proportional Hazards Models

Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.

Autors: Xu RF; School of Mental Health, Wenzhou Medical University, Wenzhou, China., Liu ZJ; School of Mental Health, Wenzhou Medical University, Wenzhou, China., Ouyang S; School of Mental Health, Wenzhou Medical University, Wenzhou, China., Dong Q; School of Mental Health, Wenzhou Medical University, Wenzhou, China., Yan WJ; School of Mental Health, Wenzhou Medical University, Wenzhou, China. yanwj@wmu.edu.cn.; Zhejiang Provincial Clinical Research Centre for Mental Health, Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, 325000, China. yanwj@wmu.edu.cn., Xu DW; School of Mental Health, Wenzhou Medical University, Wenzhou, China. xdw@wmu.edu.cn.; Zhejiang Provincial Clinical Research Centre for Mental Health, Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, 325000, China. xdw@wmu.edu.cn.

Font: BMC psychiatry [BMC Psychiatry] 2025 Mar 26; Vol. 25 (1), pp. 286. Date of Electronic Publication: 2025 Mar 26.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100968559 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-244X (Electronic) Linking ISSN: 1471244X NLM ISO Abbreviation: BMC Psychiatry Subsets: MEDLINE

Terms MeSH: Machine Learning* , Psychiatric Status Rating Scales*/standards , Depression*/diagnosis, Humans ; Female ; Male ; Adolescent ; China ; Child ; Mass Screening/methods ; Adult ; Psychometrics/methods ; Middle Aged ; Sensitivity and Specificity ; Depressive Disorder/diagnosis

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

Autors: Li Q; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China.; Department of Hematology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China., Lin N; Department of Internal Medicine, Longmatan District Hospital of Traditional Chinese Medicine, Luzhou, Sichuan, 646000, China., Wang Z; Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Chen Y; The Affiliated Hospital of Traditional Chinese Medicine, Southwest Medical University, Luzhou, Sichuan, 646000, China., Xie Y; Department of lmmunology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi, 530021, China., Wang X; Department of Hematology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China., Tang J; Department of Hematology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China., Xu Y; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Xu M; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Lu N; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Huang Y; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Luo J; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China., Liu Z; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, China. liuzhenfang@gxmu.edu.cn., Jing L; Department of Hematology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China. 2251270990@qq.com.

Font: BMC infectious diseases [BMC Infect Dis] 2025 Mar 26; Vol. 25 (1), pp. 415. Date of Electronic Publication: 2025 Mar 26.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100968551 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2334 (Electronic) Linking ISSN: 14712334 NLM ISO Abbreviation: BMC Infect Dis Subsets: MEDLINE

Terms MeSH: Hematologic Neoplasms*/complications , Machine Learning* , Cytokines*/blood , Th2 Cells*/immunology , Th1 Cells*/immunology, Humans ; Male ; Female ; Middle Aged ; Retrospective Studies ; Prognosis ; Aged ; Adult ; Bacteremia/mortality ; Bacteremia/microbiology ; Bacteremia/immunology ; Risk Factors

Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.

Autors: Setegn GM; Department of Computer Science, Debark University, Debark, Ethiopian, Ethiopia. Gizachew.Mulu@dku.edu.et., Dejene BE; Department of Information Science, University of Gondar, Gondar, Ethiopian, Ethiopia.

Font: BMC infectious diseases [BMC Infect Dis] 2025 Mar 26; Vol. 25 (1), pp. 419. Date of Electronic Publication: 2025 Mar 26.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100968551 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2334 (Electronic) Linking ISSN: 14712334 NLM ISO Abbreviation: BMC Infect Dis Subsets: MEDLINE

Terms MeSH: Mpox, Monkeypox*/diagnosis , Mpox, Monkeypox*/epidemiology , Machine Learning*, Humans ; Artificial Intelligence ; Animals ; Disease Outbreaks/prevention & control ; Monkeypox virus/genetics

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

Autors: Maroufi SS; Department of Anesthesia, Faculty of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran., Movahed MS; Education Development Center, Iran University of Medical Sciences, Tehran, Iran., Ejmalian A; Department of Anesthesiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran., Sarkhosh M; Department of Anesthesia, Faculty of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran. Maryamsarkhosh1999@gmail.com., Behmanesh A; Education Development Center, Iran University of Medical Sciences, Tehran, Iran. aa.behmanesh@gmail.com.; Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. aa.behmanesh@gmail.com.

Font: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 Mar 25; Vol. 25 (1), pp. 146. Date of Electronic Publication: 2025 Mar 25.

Tipologia de la publicació: Journal Article; Comparative Study

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE

Terms MeSH: Machine Learning* , Patient Discharge*, Humans ; Male ; Cross-Sectional Studies ; Female ; Middle Aged ; Adult ; Aged ; Algorithms ; Recovery Room ; Anesthesia, General

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.

Autors: Raghareutai K; Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand., Tanchotsrinon W; Siriraj Informatics and Data Innovation Center, Siriraj Hospital, Mahidol University, Bangkok, Thailand., Sattayalertyanyong O; Siriraj GI endoscopy Center, Siriraj Hospital, Bangkok, Thailand., Kaosombatwattana U; Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand. uayporn.kao@mahidol.ac.th.; Siriraj GI endoscopy Center, Siriraj Hospital, Bangkok, Thailand. uayporn.kao@mahidol.ac.th.

Font: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 Mar 24; Vol. 25 (1), pp. 145. Date of Electronic Publication: 2025 Mar 24.

Tipologia de la publicació: Journal Article; Validation Study

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE

Terms MeSH: Gastrointestinal Hemorrhage*/therapy , Machine Learning*, Humans ; Male ; Female ; Middle Aged ; Aged ; Retrospective Studies ; Acute Disease ; Adult

Understanding EMS response times: a machine learning-based analysis.

Autors: Hill P; Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden. peter.hill@ki.se.; Karolinska Institutet, Department of Clinical Science and Education, Södersjukhuset in Stockholm, Stockholm, SE-118 83, Sweden. peter.hill@ki.se., Lederman J; Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.; Karolinska Institutet, Department of Clinical Science and Education, Södersjukhuset in Stockholm, Stockholm, SE-118 83, Sweden., Jonsson D; KTH Royal Institute of Technology, The Department of Urban Planning and Environment, Stockholm, Sweden., Bolin P; Region Stockholm Health and Medical Care Administration, The Department for Specialized Care, Stockholm, Sweden.; Karolinska Institutet, Department of Learning, Informatics, Management and Ethics (LIME), Solna, Sweden., Vicente V; The Ambulance Medical Service in Stockholm (AISAB), Stockholm, Sweden.; Academic EMS in Stockholm, Stockholm, Sweden.; Karolinska Institutet, Department of Clinical Science and Education, Södersjukhuset in Stockholm, Stockholm, SE-118 83, Sweden.

Font: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 Mar 24; Vol. 25 (1), pp. 143. Date of Electronic Publication: 2025 Mar 24.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE

Terms MeSH: Machine Learning* , Emergency Medical Services*, Humans ; Retrospective Studies ; Sweden ; Time Factors

Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.

Autors: Chen T; Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., Zhu H; School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China., Hu Y; Department of Radiology, Shenzhen Children's Hospital, China Medical University, Shenzhen, China., Huang Y; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., He W; Shenzhen ZhenData Intelligent Technology Co., Ltd, Shenzhen, China., Luo Y; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., Wu Z; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., Fang D; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., Sun L; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China., Zeng H; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China. homerzeng@126.com., Li Z; Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China. li_33@126.com.

Font: BMC musculoskeletal disorders [BMC Musculoskelet Disord] 2025 Mar 22; Vol. 26 (1), pp. 287. Date of Electronic Publication: 2025 Mar 22.

Tipologia de la publicació: Journal Article

Informació de la publicació: Publisher: BioMed Central Country of Publication: England NLM ID: 100968565 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2474 (Electronic) Linking ISSN: 14712474 NLM ISO Abbreviation: BMC Musculoskelet Disord Subsets: MEDLINE

Terms MeSH: Muscular Dystrophy, Duchenne*/diagnostic imaging , Magnetic Resonance Imaging*/methods , Machine Learning*, Humans ; Retrospective Studies ; Child, Preschool ; Male ; Diagnosis, Differential ; Female ; Deep Learning ; Radiomics