{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T23:12:37Z","timestamp":1790637157588,"version":"4.1.0"},"reference-count":34,"publisher":"American Medical Association (AMA)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Many treatments exist for depression, yet none are universally effective. Multivariable predictive models support personalized treatment selection.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To develop a model predicting response to internet-delivered cognitive behavioral therapy (iCBT) and test its treatment specificity against antidepressant medications.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n The Precision in Psychiatry prognostic study was a 4-week study collecting extensive baseline self-report and cognitive data online to predict early iCBT response, from February 2019 to May 2022. Patients in the iCBT group were recruited via an Irish mental health charity and a UK NHS Talking Therapies clinic. A separate antidepressant group was recruited globally online and via print advertisements. Participants were aged 18 to 70 years, fluent in English, had computer access, started treatment within 2 days of enrollment, and scored at least 10 on the Work and Social Adjustment Scale. Analysis was completed in December 2024.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n Low-intensity, clinician-guided iCBT with multimedia psychoeducation. Patients receiving antidepressants primarily received selective serotonin-reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Machine learning models were trained using the iCBT sample to predict change in depression severity (16-item Quick Inventory of Depressive Symptomatology\u2013Self Report) at week 4. The best model was tested on holdout iCBT and antidepressant data. A separate model was trained on patients receiving iCBT only to assess treatment-specificity.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Of 2674 patients screened, 883 completed baseline and final assessments, with 776 patients receiving iCBT (mean [SD] age, 31.8 [11.0] years; 600 [77.5%] female) and 107 patients receiving antidepressant medication (mean [SD] age, 30.1 [10.4] years; 78 [72.9%] female). Both samples had some treatment overlap (24% and 34%, respectively). Elastic net regression with 27 predictors best explained the variance in depression change (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200914%; SD, 0.8%; 95% CI, 13.8%-14.2%). Key predictors included baseline depression, treatment expectation, transdiagnostic symptoms, and, less strongly, cognitive variables. The model performed well on holdout iCBT (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200918.8%; root mean square error [RMSE], 0.88) and antidepressant (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200917.9%; RMSE, 1.10) data. Retraining on 181 patients who received iCBT only increased treatment specificity in predictions (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200919.3%; RMSE, 0.89) vs 71 patients who received antidepressants only (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u200910.8%; RMSE, 1.17).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n This prognostic study in a naturalistic setting found that self-reported data predicted iCBT response better than cognitive data. Model predictions generalized to patients receiving antidepressants, some of whom also received psychotherapy. Training models on single-treatment cohorts may yield more treatment-specific predictions.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.41639","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T16:31:05Z","timestamp":1762446665000},"page":"e2541639","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication"],"prefix":"10.1001","volume":"8","author":[{"given":"Chi Tak","family":"Lee","sequence":"first","affiliation":[{"name":"School of Psychology, Trinity College Dublin, Dublin, Ireland"},{"name":"Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland"},{"name":"Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland"},{"name":"Amwell Science, Amwell, Dublin, 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