{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:52:27Z","timestamp":1775595147191,"version":"3.50.1"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008914","name":"Ontario Brain Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008914","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["GAC-154985"],"award-info":[{"award-number":["GAC-154985"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013327","name":"H. Lundbeck A\/S","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013327","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Bristol-Myers Squibb"},{"DOI":"10.13039\/100004319","name":"Pfizer","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100011725","name":"Servier","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100011725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objectives<\/jats:title>\n<jats:p>Antidepressants are first-line treatments for major depressive disorder (MDD), but 40\u201360% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>We replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (\u226550% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score \u22645) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Our replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>We successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0253023","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T17:32:14Z","timestamp":1624901534000},"page":"e0253023","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":24,"title":["Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1602-6382","authenticated-orcid":true,"given":"John-Jose","family":"Nunez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teyden T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond T.","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benicio N.","family":"Frey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roumen","family":"Milev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel J.","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susan","family":"Rotzinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio N.","family":"Soares","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rudolf","family":"Uher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidney H.","family":"Kennedy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond W.","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"pone.0253023.ref001","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1037\/a0015416","article-title":"Depression and Everyday Social Activity, Belonging, and Well-Being","volume":"56","author":"MF Steger","year":"2009","journal-title":"J Couns Psychol"},{"key":"pone.0253023.ref002","doi-asserted-by":"crossref","first-page":"e69637","DOI":"10.1371\/journal.pone.0069637","article-title":"The Epidemiological Modelling of Major Depressive Disorder: Application for the Global Burden of Disease Study 2010","volume":"8","author":"AJ Ferrari","year":"2013","journal-title":"PLOS ONE"},{"key":"pone.0253023.ref003","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1001\/jamapsychiatry.2014.2502","article-title":"Mortality in Mental Disorders and Global Disease Burden Implications: A Systematic Review and Meta-analysis","volume":"72","author":"ER Walker","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"pone.0253023.ref004","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/S0140-6736(15)00128-2","article-title":"Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990\u20132013: a systematic analysis for the Global Burden of Disease Study 2013","volume":"386","author":"MH Forouzanfar","year":"2015","journal-title":"The Lancet"},{"key":"pone.0253023.ref005","unstructured":"Depression Treatment after Unsatisfactory Response to SSRIs when used as First-line Therapy | Effective Health Care Program. 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