ArticleJournal of anxiety disorders2018
Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.
Article in Journal of anxiety disorders, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- 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
- Scalable prediction of suicidal risk in university students: a three steps machine learning approach in university settings.Scientific reports · 2026Article
- Impact of self-reported cannabis use on veterans' intensive PTSD treatment outcomes.Psychological trauma : theory, research, practice and policy · 2026Article
- Development and external validation of a short prognostic screening instrument for PTSD one year following individual civilian trauma.European journal of psychotraumatology · 2025Article
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Three distinct patterns of mental health response following accidents in mountain sports: a follow-up study of individuals treated at a tertiary trauma center.European archives of psychiatry and clinical neuroscience · 2024Article
- Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.Scientific reports · 2024Article
- Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.Journal of anxiety disorders · 2024Article
- Mental health phenotypes of well-controlled HIV in Uganda.Frontiers in public health · 2024Article
- Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort.Nature. Mental health · 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
- Protocol for a randomized controlled trial in Cambodian individuals with PTSD: Trauma-Informed Treatment Algorithms for Advancing Novel Outcomes (Project TITAN).Contemporary clinical trials · 2023Article
- Derivation and Validation of a Brief Emergency Department-Based Prediction Tool for Posttraumatic Stress After Motor Vehicle Collision.Annals of emergency medicine · 2023Article
- Predicting Posttraumatic Stress Disorder Among Survivors of Recent Interpersonal Violence.Journal of interpersonal violence · 2022Article
- Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom.Computers in human behavior · 2022Article
- Seeing the forest for the trees: Predicting attendance in trials for co-occurring PTSD and substance use disorders with a machine learning approach.Journal of consulting and clinical psychology · 2021Article
- Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD screening status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.
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What OpenQuestion holds
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.