{"title":"Machine learning in mental health — getting better all the time","authors":"","doi":"10.1038/s44220-024-00383-2","DOIUrl":null,"url":null,"abstract":"Machine learning for mental health and psychiatry research has emerged as a powerful set of tools for harnessing increased computing power to analyze relationships in massive and complex datasets. These findings are ultimately poised to help inform research directions, the diagnosis and prediction of psychopathology, and clinical recommendations for treating mental health disorders.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00383-2.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature mental health","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44220-024-00383-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning for mental health and psychiatry research has emerged as a powerful set of tools for harnessing increased computing power to analyze relationships in massive and complex datasets. These findings are ultimately poised to help inform research directions, the diagnosis and prediction of psychopathology, and clinical recommendations for treating mental health disorders.
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