{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T18:43:39Z","timestamp":1788288219739,"version":"build-2803163510"},"reference-count":36,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["R21DA051802"],"award-info":[{"award-number":["R21DA051802"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Traumatic Stress"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress disorder (PTSD) symptom escalations. This prospective, intensive longitudinal study aimed to evaluate whether combining baseline (static) and daily diary (dynamic) predictors with machine learning can help forecast clinically significant PTSD symptom increases among veterans. Participants were 74 recently discharged U.S. veterans (\n                    <jats:italic>M<\/jats:italic>\n                    <jats:sub>age<\/jats:sub>\n                    = 33.5 years) who completed twice\u2010daily diary surveys for up to 87 days via a mobile app, yielding 4,307 diary days. The outcome was a binary indicator of clinically significant daily PTSD symptom increase (&gt; 1.0 standard deviation above a participant's individual mean over the first 2 study weeks). Random forest models identified top predictors; LASSO regression estimated effect sizes among top predictors. Daily negative affect was the top predictive variable,\n                    <jats:italic>OR<\/jats:italic>\n                    = 1.33, retained in 100% of LASSO iterations. Daily depressed mood,\n                    <jats:italic>OR<\/jats:italic>\n                    = 1.35; anxious mood,\n                    <jats:italic>OR<\/jats:italic>\n                    = 1.15; and perceived stress,\n                    <jats:italic>OR<\/jats:italic>\n                    = 1.13, were also reliably retained. Variables involving alcohol, cannabis use, and baseline impulsivity were less robust but remained prominent predictors of PTSD symptom escalations. Post hoc interaction analyses showed that co\u2010occurring high negative affect and anxiety yielded a &gt; 55% probability of PTSD symptom escalation. The findings show that daily affective states, especially negative mood and stress, strongly predict PTSD symptom increases in veterans. Using machine learning and high\u2010frequency tracking, advances in personalized, real\u2010time PTSD care are possible. Findings support just\u2010in\u2010time interventions for when veterans need help most: in the moment.\n                  <\/jats:p>","DOI":"10.1002\/jts.70036","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:19:05Z","timestamp":1767057545000},"page":"319-329","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real\u2010time daily data with supervised learning"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6108-4936","authenticated-orcid":false,"given":"Jordan P.","family":"Davis","sequence":"first","affiliation":[{"name":"RAND Corporation  Santa Monica California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Prindle","sequence":"additional","affiliation":[{"name":"Suzanne Dworak\u2010Peck School of Social Work University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric R.","family":"Pedersen","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Leightley","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, School of Life Course &amp; Population Sciences King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bistra","family":"Dilkina","sequence":"additional","affiliation":[{"name":"Viterbi School of Engineering University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Dworkin","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaddy","family":"Saba","sequence":"additional","affiliation":[{"name":"Silver School of Social Work New York University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Praneeth","family":"Thota","sequence":"additional","affiliation":[{"name":"Viterbi School of Engineering University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sriram","family":"Nuthi","sequence":"additional","affiliation":[{"name":"Viterbi School of Engineering University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark A.","family":"Prince","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angeles","family":"Sedano","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association. 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Bovin M. J. Kimerling R. Kaloupek D. G Marx B. P. Pless Kaiser A. &Schnurr P. P.(2015).Primary Care PTSD Screen for DSM\u20105 (PC\u2010PTSD\u20105).https:\/\/www.ptsd.va.gov\/professional\/assessment\/screens\/pc\u2010ptsd.asp","DOI":"10.1037\/t92032-000"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2019.12.001"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15327752jpa6601_2"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.2307\/2531894"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1097\/PSY.0000000000001265"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.166.10.1092"},{"key":"e_1_2_9_29_1","unstructured":"Substance Abuse and Mental Health Services Administration. 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