{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T22:13:30Z","timestamp":1777414410669,"version":"3.51.4"},"reference-count":48,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100017475","name":"General Sir John Monash Foundation","doi-asserted-by":"publisher","award":["Tim Fisher John Monash Scholarship"],"award-info":[{"award-number":["Tim Fisher John Monash Scholarship"]}],"id":[{"id":"10.13039\/501100017475","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Aust N Z J Psychiatry"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p>Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p\u2009&lt;\u20090.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p\u2009=\u20090.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.<\/jats:p>","DOI":"10.1177\/00048674231158267","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T01:28:09Z","timestamp":1677202089000},"page":"1316-1323","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine learning prediction will be part of future treatment of depression"],"prefix":"10.1177","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7097-3666","authenticated-orcid":false,"given":"Matthew J","family":"Lennon","sequence":"first","affiliation":[{"name":"Department of Psychiatry, University of Oxford, Oxford, UK"},{"name":"Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Harmer","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of Oxford, Oxford, UK"},{"name":"Oxford Health NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"bibr1-00048674231158267","doi-asserted-by":"publisher","DOI":"10.1080\/10503307.2020.1808729"},{"key":"bibr2-00048674231158267","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00438-z"},{"key":"bibr3-00048674231158267","unstructured":"Australian Institute of Health and Welfare (2022a) Australia\u2019s Health 2022, Australia\u2019s Health. 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