{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T07:31:39Z","timestamp":1776411099750,"version":"3.51.2"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Introduction: Early detection and intervention are crucial for reducing the impacts of depression and associated healthcare costs. Few studies have used electronic health records (EHR) and machine learning (ML) with a longitudinal design to predict depression onset. We developed and validated ML algorithms using EHR to identify patients at high risk for the onset of diagnosis-based major depressive disorder (MDD) in primary care settings. Methods: Using a prognostic modeling approach with retrospective cohort study design, we identified patient visits in primary care settings for individuals aged \u226518 years from the Accelerating Data Value Across a National Community Health Center Network Clinical Research Network 2015\u20132021 data. We measured 267 features at six-month intervals starting six months prior to the first encounter. We developed algorithms using Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and XGBoost with 10-fold cross validation. Using hold-out testing data, we measured prediction performance (e.g., C-statistics), stratified patients into decile risk subgroups, and assessed model biases. Results: Among eligible 1,965,399 individuals (mean age = 43.52 \u00b1 16.04 years; male = 35%; African American = 20%) with 4,985,280 person-periods, the MDD onset rate was 1% during the study period. XGBoost performed similarly to other models and had the fewest predictors, (C-statistic = 0.763, 95% CI = [0.760, 0.767]). XGBoost had a 66.78% sensitivity, 74.19% specificity, and 2.55% positive predictive value at the balanced threshold identified using Youdan Index. The top three risk decile subgroups captured \u223c70% of MDD cases, without significant racial or sex biases. Conclusions: An ML algorithm using EHR data can effectively identify individuals at high risk of depression onset within the subsequent six months, without exacerbating racial or sex biases, providing a valuable tool for targeted early interventions.<\/jats:p>","DOI":"10.3233\/shti250989","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:24Z","timestamp":1754566644000},"source":"Crossref","is-referenced-by-count":1,"title":["Development and Validation of Machine-Learning Algorithms to Predict the Onset of Depression Using Electronic Health Record Data: A Prognostic Modeling Study"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5292-2486","authenticated-orcid":false,"given":"Frances R.","family":"Chen","sequence":"first","affiliation":[{"name":"Georgia State University Andrew Young School of Policy Studies, Atlanta, GA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8286-4560","authenticated-orcid":false,"given":"James L.","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Florida, College of Pharmacy, Gainesville, FL USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1640-5497","authenticated-orcid":false,"given":"Debbie L.","family":"Wilson","sequence":"additional","affiliation":[{"name":"University of Florida, College of Pharmacy, Gainesville, FL USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6590-4770","authenticated-orcid":false,"given":"Wei-Hsuan Jenny","family":"Lo-Ciganic","sequence":"additional","affiliation":[{"name":"University of Pittsburgh School of Medicine, Pittsburgh, PA USA"},{"name":"North Florida\/South Georgia Veterans Health System, Geriatric Research Education and Clinical Center, Gainesville, FL USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250989","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:25Z","timestamp":1754566645000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250989","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}