ArticleJMIR mental health2019
Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach.
Article in JMIR mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Can machine learning predict PTSD symptoms from trauma narratives of children and adolescents?European journal of psychotraumatology · 2025Article
- A systematic review of machine learning findings in PTSD and their relationships with theoretical models.Nature. Mental health · 2025Article
- Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.BMC psychiatry · 2024Article
- Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.JMIR mental health · 2024Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.Journal of anxiety disorders · 2024Article
- The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments.Frontiers in psychiatry · 2023Article
- Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling.Frontiers in psychology · 2023Article
- Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach.JMIR mental health · 2022Article
- Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.Psychiatry and clinical neurosciences · 2022Article
- Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study.JMIR mHealth and uHealth · 2021Article
- Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.JMIR mental health · 2021Article
- Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study.Neurobiology of stress · 2021Article
- The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach.Journal of affective disorders · 2021Article
- Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders.Frontiers in psychiatry · 2021Article
- Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning.BMC psychiatry · 2020Article
- Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial.JMIR research protocols · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD).
objectiveIdentifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions.
methodsWe proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones.
resultsWe show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma.
conclusionsThese results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention.
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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.