{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mistry S"],"funding":["National Center for Advancing Translational Sciences","NCATS NIH HHS","NIDDK NIH HHS","National Institute of Diabetes and Digestive and Kidney Diseases","U.S. National Library of Medicine","Israel National Road Safety Authority","NLM NIH HHS"],"pagination":["102461"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9834645"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["135"],"pubmed_abstract":["<h4>Background</h4>Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.<h4>Objective</h4>We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.<h4>Methods</h4>We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes eti"],"journal":["Artificial intelligence in medicine"],"pubmed_title":["Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review."],"pmcid":["PMC9834645"],"funding_grant_id":["T15LM007124","5T32DK110966-04","T32 DK110966","UL1 TR001067","T15 LM007124","F30 DK134113","F30DK134113","UL1TR002538","UL1 TR002538"],"pubmed_authors":["Gouripeddi R","Facelli JC","Mistry S","Riches NO"],"additional_accession":[]},"is_claimable":false,"name":"Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.","description":"<h4>Background</h4>Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed.<h4>Objective</h4>We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction.<h4>Methods</h4>We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes eti","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-29T09:54:13.418Z","creation":"2025-04-06T19:20:15.975Z"},"accession":"S-EPMC9834645","cross_references":{"pubmed":["36628796"],"doi":["10.1016/j.artmed.2022.102461"]}}