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Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80\/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Among participants, 78% identified as white with an average age of 30 [IQR 26\u201334]; 61% had income \u2265 $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74\u20130.89, sensitivity 0.35\u20130.81, specificity 0.78\u20130.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC\u2009=\u20090.89, sensitivity\u2009=\u20090.81, specificity\u2009=\u20090.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC\u2009=\u20090.93) and simpler (9 variables).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00737-024-01474-w","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T05:01:40Z","timestamp":1716354100000},"page":"1019-1031","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-2230","authenticated-orcid":false,"given":"Tamar","family":"Krishnamurti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samantha","family":"Rodriguez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryan","family":"Wilder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priya","family":"Gopalan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyagriv N.","family":"Simhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"1474_CR1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jad.2015.12.008","volume":"192","author":"AA Abajobir","year":"2016","unstructured":"Abajobir AA, Maravilla JC, Alati R, Najman JM (2016) A systematic review and meta-analysis of the association between unintended pregnancy and perinatal depression. 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