ArticleTranslational psychiatry2017
Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.
Article in Translational psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 139 citations in OpenAlex.
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Predicting longitudinal post-traumatic stress disorder symptoms using psychological measures and heart rate variability: a machine-learning approach.European journal of psychotraumatology · 2026Article
- Elevated rates of adverse PTSD symptom trajectories in a civilian mass-trauma cohort.European journal of psychotraumatology · 2026Article
- Large-Scale Data Analysis of Post-Traumatic Stress Disorder (PTSD) Through GWAS Fine-Mapping and Systems Biology.Journal of personalized medicine · 2026Article
- Causal Discovery in Observational Medical Research: Scoping Review.JMIR medical informatics · 2026Article
- Growth Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes.Transplantation direct · 2026Article
- Posttraumatic stress in young children at risk for maltreatment: a causal data science analysis.Frontiers in health services · 2026Article
- Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.European journal of psychotraumatology · 2025Article
- Development and external validation of a short prognostic screening instrument for PTSD one year following individual civilian trauma.European journal of psychotraumatology · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.Journal of anxiety disorders · 2024Article
- Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression.BMC medical informatics and decision making · 2024Article
- Half a Century of Research on Posttraumatic Stress Disorder: A Scientometric Analysis.Current neuropharmacology · 2024Article
- Predictors of 2-Year Trajectory of Post-Traumatic Stress Disorder Following Physical Injury.Depression and anxiety · 2024Article
- Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis.Translational psychiatry · 2023Article
- Adolescent, parent, and provider attitudes toward a machine-learning based clinical decision support system for selecting treatment for youth depression.Research square · 2023Article
- A computational model for learning from repeated traumatic experiences under uncertainty.Cognitive, affective & behavioral neuroscience · 2023Article
- Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19.Brain sciences · 2023Article
- Single classifier vs. ensemble machine learning approaches for mental health prediction.Brain informatics · 2023Article
- Machine learning prediction of posttraumatic stress disorder trajectories following traumatic injury: Identification and validation in two independent samples.Journal of traumatic stress · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.