ArticleBMC psychiatry2022
Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.
Article in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction models for postpartum post-traumatic stress disorder: a systematic review and meta-analysis.Archives of women's mental health · 2026Pooled it
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Autism, Obesity, and PTSD Among Adolescents and Young Adults: An Analysis of National Medicaid Claims Data.Journal of autism and developmental disorders · 2026Article
- The Moderating Role of Psychological Well-Being on the Association Between the Severity of Symptoms and Sociodemographic Factors Among Patients With Probable PTSD in Jordan: A Predictive Cross-Sectional Study.Health science reports · 2026Article
- Prediction model to identify patients with hypereosinophilic syndrome using real-world data.The journal of allergy and clinical immunology. Global · 2026Article
- Article
- Social media impact and smartwatch monitoring: Prevalence and early markers of PTSD and anxiety following mass traumatic events.PLOS mental health · 2025Article
- Assessing the feasibility, acceptability, and preliminary efficacy of the 'TraumaRelief' app for PTSD symptom management in Turkey: Study protocol for a pilot randomized controlled trial.Internet interventions · 2024Article
- Heart rate variability wrist-wearable biomarkers identify adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure.Psychiatry research · 2024Observational
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.Npj mental health research · 2023Review
- Ethical and Legal Aspects of Trauma Evaluation.Focus (American Psychiatric Publishing) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population.
methodsThe IBM® MarketScan® Commercial Subset (10/01/2015-12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non-trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort).
resultsA total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. Mean per-patient-per-6-month healthcare costs were similar between the Actual Positive and Likely PTSD cohorts ($11,156 and $11,723) and were higher than those of the Without PTSD cohort ($3616); however, cost drivers differed between cohorts, with the Likely PTSD cohort experiencing more inpatient admissions and less outpatient visits than the Actual Positive PTSD cohort.
conclusionsThese findings suggest that the lack of a PTSD diagnosis and targeted management of PTSD may result in a greater burden among undiagnosed patients and highlights the need for increased awareness of PTSD in clinical practice and among the civilian population.
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