Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out.

Författarna: Williams B; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.; Zoological Society of London, Regents Park, London, United Kingdom., Balvanera SM; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom., Sethi SS; Department of Life Sciences, Imperial College London, London, United Kingdom., Lamont TAC; Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom., Jompa J; Graduate School, Universitas Hasanuddin, Makassar, Indonesia., Prasetya M; MARS Sustainable Solutions, Makassar, Indonesia., Richardson L; School of Ocean Sciences, Bangor University, Askew Street, Menai Bridge, Anglesey, United Kingdom., Chapuis L; School of Biological Sciences, University of Bristol, Bristol, United Kingdom., Weschke E; School of Biological Sciences, University of Bristol, Bristol, United Kingdom., Hoey A; Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia., Beldade R; Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia.; Estación Costera de Investigaciones Marinas, Millennium Nucleus for Ecology and Conservation of Temperate Mesophotic Reef Ecosystems, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile., Mills SC; CRIOBE, PSL Research University, Moorea, French Polynesia.; Laboratoire d'Excellence 'CORAIL', Perpignan, France., Haguenauer A; CRIOBE, PSL Research University, Moorea, French Polynesia., Zuberer F; CRIOBE, PSL Research University, Moorea, French Polynesia., Simpson SD; School of Biological Sciences, University of Bristol, Bristol, United Kingdom., Curnick D; Zoological Society of London, Regents Park, London, United Kingdom., Jones KE; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.

Källa: PLoS computational biology [PLoS Comput Biol] 2025 Apr 28; Vol. 21 (4), pp. e1013029. Date of Electronic Publication: 2025 Apr 28 (Print Publication: 2025).

Utgivningstyp: Journal Article

Tidskrift info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE

MeSH-termer: Coral Reefs* , Unsupervised Machine Learning* , Artificial Intelligence*, Animals ; Acoustics ; Ecosystem ; Algorithms ; Neural Networks, Computer ; Computational Biology ; Fishes/physiology ; Machine Learning ; Environmental Monitoring/methods

Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy.

Författarna: Mishra A; School of Industrial and Information Engineering, Politecnico Di Milano, Milan, Italy., Jatt V; Department of Mechanical Engineering, School of Engineering and Applied Sciences, Bennett University, Noida, Uttar Pradesh, India., Sefene EM; Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan., Salunkhe S; Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.; Department of Mechanical Engineering, Gazi University Faculty of Engineering, Maltepe, Ankara, Turkey., Cep R; Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, Czech Republic., Abouel Nasr E; Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia.

Källa: PloS one [PLoS One] 2025 Apr 04; Vol. 20 (4), pp. e0309751. Date of Electronic Publication: 2025 Apr 04 (Print Publication: 2025).

Utgivningstyp: Journal Article

Tidskrift 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-termer: Alloys*/chemistry , Aluminum*/chemistry , Supervised Machine Learning*, Friction ; Temperature ; Algorithms ; Neural Networks, Computer ; Machine Learning

Explainable machine learning for predictive modeling of blowing snow detection and meteorological feature assessment using XGBoost-SHAP.

Författarna: Wang F; School of Civil Engineering, Beijing Jiaotong University, Beijing, China.; Guoneng Shuohuang Railway Development Co., LTD, Yuanping, Shanxi Province, China., Wang X; Guoneng Shuohuang Railway Development Co., LTD, Yuanping, Shanxi Province, China., Li S; School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, Hebei Province, China.

Källa: PloS one [PLoS One] 2025 Mar 28; Vol. 20 (3), pp. e0318835. Date of Electronic Publication: 2025 Mar 28 (Print Publication: 2025).

Utgivningstyp: Journal Article

Tidskrift 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-termer: Snow* , Machine Learning* , Models, Theoretical*, Wind ; Forecasting ; Weather ; Temperature ; Meteorological Concepts ; Humidity ; Boosting Machine Learning Algorithms

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

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift 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-termer: Sustainable Development*/trends , Unsupervised Machine Learning*, Humans ; Goals ; United Nations ; Socioeconomic Factors ; Machine Learning

Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

Författarna: Mochurad L; Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine. lesia.i.mochurad@lpnu.ua., Babii V; Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine., Boliubash Y; Artificial Intelligence Department, Lviv Polytechnic National University, 12 S. Bandery St, Lviv, 79013, Ukraine., Mochurad Y; Danylo Halytsky Lviv National Medical University, 69 Pekarska Street, Lviv, 79010, Ukraine.

Källa: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 Feb 07; Vol. 25 (1), pp. 63. Date of Electronic Publication: 2025 Feb 07.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Stroke*/diagnosis , Principal Component Analysis* , Artificial Intelligence* , Machine Learning*, Humans ; Risk Assessment/methods ; Boosting Machine Learning Algorithms

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

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: 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

Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

Författarna: Khurshid MR; Department of Statistics, Islamia University College, Peshawar, Khyber Pakhtunkhwa, Pakistan., Manzoor S; Department of Statistics, Islamia University College, Peshawar, Khyber Pakhtunkhwa, Pakistan., Sadiq T; Centre for Artificial Intelligence Research (CAIR), Department of Information and Communication Technology, University of Agder, Kristiansand, Grimstad, Norway., Hussain L; Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, Pakistan.; Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan., Khan MS; Children's National Hospital, Washington, DC, United States of America., Dutta AK; Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Kingdom of Saudi Arabia.

Källa: PloS one [PLoS One] 2025 Jan 24; Vol. 20 (1), pp. e0310218. Date of Electronic Publication: 2025 Jan 24 (Print Publication: 2025).

Utgivningstyp: Journal Article

Tidskrift 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-termer: Machine Learning* , Diabetes Mellitus*/epidemiology , Diabetes Mellitus*/diagnosis, Bayes Theorem ; Humans ; Algorithms ; Female ; Male ; Boosting Machine Learning Algorithms

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

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift 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-termer: 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.

Författarna: 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.

Källa: 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).

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: 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.

Författarna: 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.

Källa: 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.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: 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.

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift info: 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

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

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

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: 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.

Författarna: 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.

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

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Algorithms* , Machine Learning*, Supervised Machine Learning

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.

Författarna: Wang X; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Chen H; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Wang L; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Sun W; Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, Room 1411, Hangzhou, Zhejiang, 310058, China. wgsun@zju.edu.cn.

Källa: BMC gastroenterology [BMC Gastroenterol] 2025 May 15; Vol. 25 (1), pp. 376. Date of Electronic Publication: 2025 May 15.

Utgivningstyp: Journal Article

Tidskrift info: Publisher: BioMed Central Country of Publication: England NLM ID: 100968547 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-230X (Electronic) Linking ISSN: 1471230X NLM ISO Abbreviation: BMC Gastroenterol Subsets: MEDLINE

MeSH-termer: Machine Learning* , Fatty Liver*/mortality , Non-alcoholic Fatty Liver Disease*/mortality, Humans ; Male ; Female ; Middle Aged ; Longitudinal Studies ; Adult ; Risk Factors ; Nutrition Surveys ; Cause of Death ; Aged ; Kaplan-Meier Estimate

Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.

Författarna: Ling T; Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China., Zuo Z; School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan province, China., Huang M; Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China., Ma J; Department of Medical Imaging, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China., Wu L; Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. wuliucheng@gxmu.edu.cn.

Källa: BMC cancer [BMC Cancer] 2025 May 06; Vol. 25 (1), pp. 834. Date of Electronic Publication: 2025 May 06.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Stomach Neoplasms*/pathology , Stomach Neoplasms*/drug therapy , Stomach Neoplasms*/diagnostic imaging , Machine Learning* , Tomography, X-Ray Computed*/methods , Biomarkers, Tumor*/analysis, Humans ; Neoadjuvant Therapy/methods ; Male ; Female ; Middle Aged ; Retrospective Studies ; Lymphatic Metastasis/diagnostic imaging ; Aged ; ROC Curve ; Adult ; Prognosis ; Lymph Nodes/pathology ; Lymph Nodes/diagnostic imaging ; Radiomics

Data-driven machine learning algorithm model for pneumonia prediction and determinant factor stratification among children aged 6-23 months in Ethiopia.

Författarna: Demsash AW; Debre Berhan University, Asrat Woldeyes Health Science Campus, Public Health Department, Debre Berihan, Ethiopia. addisalemworkie599@gmail.com., Abebe R; Debre Berhan University, Asrat Woldeyes Health Science Campus, Public Health Department, Debre Berihan, Ethiopia., Gezimu W; Mattu University, Health Science College, Mettu, Ethiopia., Kitil GW; Mattu University, Health Science College, Mettu, Ethiopia., Tizazu MA; Debre Berhan University, Asrat Woldeyes Health Science Campus, Public Health Department, Debre Berihan, Ethiopia., Lembebo A; Debre Berhan University, Asrat Woldeyes Health Science Campus, Public Health Department, Debre Berihan, Ethiopia., Bekele F; Wallaga University, Health Science College, Nekemte, Ethiopia., Alemu SS; Madda Walabu University, Health Science College, Shashemene Campus, Shashemene, Ethiopia., Jarso MH; Madda Walabu University, Health Science College, Shashemene Campus, Shashemene, Ethiopia., Dube G; Debre Berhan University, Asrat Woldeyes Health Science Campus, Public Health Department, Debre Berihan, Ethiopia., Wedajo LF; Wallaga University, Health Science College, Nekemte, Ethiopia., Purohit S; Department of Environmental/Ecological Studies and Sustainability, Akamai University, Kamuela, USA.; Women Researchers Council, Azerbaijan State University of Economics, Baku, Azerbaijan., Kalayou MH; Health Science Campus, Health Informatics Department, Wollo University, Wollo, Ethiopia.

Källa: BMC infectious diseases [BMC Infect Dis] 2025 May 02; Vol. 25 (1), pp. 647. Date of Electronic Publication: 2025 May 02.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Pneumonia*/epidemiology , Pneumonia*/diagnosis , Machine Learning*, Humans ; Ethiopia/epidemiology ; Infant ; Female ; Male ; Algorithms ; Risk Factors

Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.

Författarna: Chen QY; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China., Yin SM; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China., Shao MM; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.; Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China., Yi FS; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China. yifengshuang@ccmu.edu.cn.; Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China. yifengshuang@ccmu.edu.cn., Shi HZ; Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China. shihuanzhong@sina.com.

Källa: Respiratory research [Respir Res] 2025 May 02; Vol. 26 (1), pp. 170. Date of Electronic Publication: 2025 May 02.

Utgivningstyp: Journal Article

Tidskrift info: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 101090633 Publication Model: Electronic Cited Medium: Internet ISSN: 1465-993X (Electronic) Linking ISSN: 14659921 NLM ISO Abbreviation: Respir Res Subsets: MEDLINE

MeSH-termer: Machine Learning* , L-Lactate Dehydrogenase*/blood , L-Lactate Dehydrogenase*/metabolism , Adenosine Deaminase*/blood , Adenosine Deaminase*/metabolism , Pleural Effusion*/diagnosis , Pleural Effusion*/etiology , Pleural Effusion, Malignant*/diagnosis , Pleural Effusion, Malignant*/etiology, Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; Aged ; Age Factors ; Adult ; Biomarkers/blood ; Aged, 80 and over ; Cohort Studies

Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model.

Författarna: Chen S; Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China., Li J; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Zhang X; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Xu W; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Qiu Z; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Yan S; Hebei Medical University, Shijiazhuang, Hebei Province, 050011, China., Zhao W; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Zhao Z; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Tian P; BGI Genomics, Shenzhen, Guangdong Province, 518083, China., Zhao Q; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China.; Clinical Transformation and Application Key Lab for Obstetrics and Gynecology, Pediatrics, and Reproductive Medicine of Jiangmen, Jiangmen, Guangdong Province, 529030, China., Zhang Q; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China., Chen W; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China., Li H; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China., Ruan X; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China., Xiao G; Department of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Zhang S; Department of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Hu L; Department of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Qin J; Department of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Huang W; Department of Medical Genetics and Prenatal Diagnosis, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Li Z; Department of Prevention and Health Care, Zhuhai Center for Maternal and Child Health Care, Zhuhai, Guangdong Province, 519000, China., Wang S; BGI Genomics, Shenzhen, Guangdong Province, 518083, China.; Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control, Changsha Hospital for Maternal & Child Health Care Affiliated to Hunan Normal University, Changsha, Hunan Province, 410007, China., Zhang R; Division of Maternal-Fetal Medicine, Shenzhen Bao' an Women's and Children's Hospital, Shenzhen, Guangdong Province, 518102, China., Huang S; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China.; Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Guangdong Province, 518712, Shenzhen, China., Wang X; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China.; Department of Blood Transfusion, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, 80 Tianedang Road, Suzhou, 518712, Jiangsu, China., Yao Y; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China., Ran J; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China., Cheng D; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China., Luo Q; School of Basic Medical Sciences, Jiamusi University, Jiamusi, Heilongjiang Province, 154007, China., Pan T; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China., Gao R; School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, Hebei Province, 050017, China., Zheng J; School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, Hebei Province, 050017, China., Wang Y; School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, Hebei Province, 050017, China., Liu C; Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China., Cao X; Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China., Zhou X; Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China., Xu N; Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China., Zhang L; International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China., Han X; International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China., Wang H; School of Computer Science, Guangzhou College of Technology and Business, Guangdong Province, 510850, China., Feng S; Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, China. 13702285687@139.com., Li S; International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China. shuyuanli816@163.com., Zhang J; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China. zhangjg@bgi.com.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China. zhangjg@bgi.com.; Hebei Medical University, Shijiazhuang, Hebei Province, 050011, China. zhangjg@bgi.com., Zhao L; Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, Hebei Province, 050011, China. zhaolijian@bgi.com.; BGI Genomics, Shenzhen, Guangdong Province, 518083, China. zhaolijian@bgi.com.; Hebei Medical University, Shijiazhuang, Hebei Province, 050011, China. zhaolijian@bgi.com., Wei F; The Genetics Laboratory, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Longgang District, Guangdong Province, 518712, China. haowei727499@163.com.

Källa: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 May 01; Vol. 25 (1), pp. 178. Date of Electronic Publication: 2025 May 01.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Pre-Eclampsia*/diagnosis , Pre-Eclampsia*/blood , Machine Learning*, Humans ; Female ; Pregnancy ; Adult ; Retrospective Studies ; Biomarkers/blood

Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.

Författarna: Pons-Suñer P; ITI, Universitat Politècnica de València, Valencia, Spain. pedropons@iti.es., Signol F; ITI, Universitat Politècnica de València, Valencia, Spain. fsignol@iti.es., Alvarez N; Hospital Universitario 12 de Octubre, Imas12, Departament of Medicine, Complutense University, Madrid, Spain., Sargas C; Instituto de Investigación Sanitaria La Fe, Valencia, Spain., Dorado S; Altum Sequencing, s.l., Computer Science and Engineering Department, Carlos III University, Madrid, Spain., Ortí JVG; Instituto de Investigación Sanitaria La Fe, Valencia, Spain., Delgado Sanchis JA; ITI, Universitat Politècnica de València, Valencia, Spain., Llop M; Instituto de Investigación Sanitaria La Fe, Valencia, Spain., Arnal L; ITI, Universitat Politècnica de València, Valencia, Spain., Llobet R; ITI, Universitat Politècnica de València, Valencia, Spain., Perez-Cortes JC; ITI, Universitat Politècnica de València, Valencia, Spain., Ayala R; Hospital Universitario 12 de Octubre, Imas12, Departament of Medicine, Complutense University, Madrid, Spain., Barragán E; Instituto de Investigación Sanitaria La Fe, Valencia, Spain.

Källa: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2025 May 01; Vol. 25 (1), pp. 179. Date of Electronic Publication: 2025 May 01.

Utgivningstyp: Journal Article

Tidskrift info: 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

MeSH-termer: Leukemia, Myeloid, Acute*/therapy , Leukemia, Myeloid, Acute*/diagnosis , Leukemia, Myeloid, Acute*/complications , Supervised Machine Learning*/standards, Humans ; Male ; Middle Aged ; Female ; Aged ; Adult ; Spain ; Young Adult

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

Författarna: Hajikarimloo B; Department of Neurological Surgery, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. bardii47@yahoo.com., Mohammadzadeh I; Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Tos SM; Department of Neurological Surgery, University of Virginia, Charlottesville, VA, USA., Habibi MA; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Hashemi R; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Hezaveh EB; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Najari D; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Hasanzade A; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Hooshmand M; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Bana S; Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Källa: BMC cancer [BMC Cancer] 2025 May 01; Vol. 25 (1), pp. 818. Date of Electronic Publication: 2025 May 01.

Utgivningstyp: Journal Article; Systematic Review; Meta-Analysis

Tidskrift info: 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

MeSH-termer: Carcinoma, Non-Small-Cell Lung*/pathology , Carcinoma, Non-Small-Cell Lung*/genetics , Carcinoma, Non-Small-Cell Lung*/secondary , Lung Neoplasms*/pathology , Lung Neoplasms*/genetics , Brain Neoplasms*/secondary , Brain Neoplasms*/genetics , Machine Learning*, Humans ; ErbB Receptors/genetics ; Prognosis ; Mutation