biostudies-literatureUnknown27Crow TMElectronic health record (EHR) data are increasingly used in predictive models of posttraumatic stress disorder (PTSD), but it is unknown how multivariable prediction of an EHR-based diagnosis might differ from prediction of a more rigorous diagnostic criterion. This distinction is important because EHR data are subject to multiple biases, including diagnostic misclassification and differential health care use resulting from factors such as illness severity. This study aims to compare predictive models using the same predictors to predict an EHR-based versus semistructured interview-based PTSD diagnostic criterion, quantify model performance discrepancies, and examine potential mechanisms that account for performance differences. We compared the performance of several machine learning modeJournal of medical Internet researche63352https://www.ebi.ac.uk/biostudies/studies/S-EPMC12384688biostudies-literatureMisleading Results in Posttraumatic Stress Disorder Predictive Models Using Electronic Health Record Data: Algorithm Validation Study.PMC12384688Harper KLLin EMarx BPCrowe MLKeane TMCrow TMfalseMisleading Results in Posttraumatic Stress Disorder Predictive Models Using Electronic Health Record Data: Algorithm Validation Study.Electronic health record (EHR) data are increasingly used in predictive models of posttraumatic stress disorder (PTSD), but it is unknown how multivariable prediction of an EHR-based diagnosis might differ from prediction of a more rigorous diagnostic criterion. This distinction is important because EHR data are subject to multiple biases, including diagnostic misclassification and differential health care use resulting from factors such as illness severity. This study aims to compare predictive models using the same predictors to predict an EHR-based versus semistructured interview-based PTSD diagnostic criterion, quantify model performance discrepancies, and examine potential mechanisms that account for performance differences. We compared the performance of several machine learning mode2025-01-01T00:00:00Z2025 Aug2026-05-27T22:28:59.707Z2026-04-08T01:53:25.245ZS-EPMC123846884086489810.2196/63352