ArticleJournal of traumatic stress2022
Machine learning prediction of posttraumatic stress disorder trajectories following traumatic injury: Identification and validation in two independent samples.
Article in Journal of traumatic stress, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Early risk factors and six month posttraumatic stress disorder symptom severity following trauma.European journal of psychotraumatology · 2026Article
- A minimal model of PTSD susceptibility and timescales.iScience · 2026Article
- Predicting Probable Persistent PTSD Following the Sewol Ferry Disaster: Development of an AI Algorithm Based on Psychological Assessments and Biological Markers.Psychiatry investigation · 2026Article
- Default mode network resting-state functional connectivity and event centrality in adult acute trauma survivors.Scientific reports · 2026Article
- Optimizing Symptom Surveys with Machine Learning to Predict PTSD One Year Post-trauma....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2026Article
- Subtypes of childhood maltreatment and posttraumatic stress disorder symptoms in an adult trauma sample: The mechanistic role of sleep.Psychological trauma : theory, research, practice and policy · 2025Article
- Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.BMC psychiatry · 2024Article
- Racial discrimination increases the risk for nonremitting posttraumatic stress disorder symptoms in traumatically injured Black individuals living in the United States.Journal of traumatic stress · 2024Article
- Trajectories of Anhedonia Symptoms after Traumatic Injury.European journal of trauma & dissociation = Revue europeenne du trauma et de la dissociation · 2024Article
- The Dichotomy of Threat and Deprivation as Subtypes of Childhood Maltreatment: Differential Functional Connectivity Patterns of Threat and Reward Circuits in an Adult Trauma Sample.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Predictors of 2-Year Trajectory of Post-Traumatic Stress Disorder Following Physical Injury.Depression and anxiety · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Due to its heterogeneity, the prediction of posttraumatic stress disorder (PTSD) development after traumtic injury is difficult. Recent machine learning approaches have yielded insight into predicting PTSD symptom trajectories. Using data collected within 1 month of traumatic injury, we applied eXtreme Gradient Boosting (XGB) to classify admitted and discharged patients (hospitalized, n = 192; nonhospitalized, n = 214), recruited from a Level 1 trauma center, according to PTSD symptom trajectories. Trajectories were identified using latent class mixed models on PCL-5 scores collected at baseline, 1-3 months posttrauma, and 6 months posttrauma. In both samples, nonremitting, remitting, and resilient PTSD symptom trajectories were identified. In the admitted patient sample, a unique delayed trajectory emerged. Machine learning classifiers (i.e., XGB) were developed and tested on the admitted patient sample and externally validated on the discharged sample with biological and clinical self-report baseline variables as predictors. For external validation sets, prediction was fair for nonremitting versus other trajectories, areas under the curve (AUC = .70); good for nonremitting versus resilient trajectories, AUCs = .73-.76; and prediction failed for nonremitting versus remitting trajectories, AUCs = .46-.48. However, poor precision (< .57) across all models suggests limited generalizability of nonremitting symptom trajectory prediction from admitted to discharged patient samples. Consistency in symptom trajectory identification across samples supports prior studies on the stability of PTSD symptom trajectories following trauma exposure; however, continued work and replication with larger samples are warranted to understand overlapping and unique predictive features of PTSD in different traumatic injury populations.
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