This paper is in the following e-collection/theme issue:

Posttraumatic Stress Disorder (PTSD) (106) Decision Support for Health Professionals (1129) Machine Learning (1373) mHealth for Data Collection and Research (907) Diagnostic Tools in Mental Health (220)

Published on in Vol 6, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13946, first published .
Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach

Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach

Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach

Authors of this article:

Safwan Wshah1 Author Orcid Image ;   Christian Skalka1 Author Orcid Image ;   Matthew Price1 Author Orcid Image
Article Authors Cited by (35) Tweetations (6) Metrics

Journals

  1. Balcombe L, De Leo D. An Integrated Blueprint for Digital Mental Health Services Amidst COVID-19. JMIR Mental Health 2020;7(7):e21718 View
  2. Jones C, Smith-MacDonald L, Miguel-Cruz A, Pike A, van Gelderen M, Lentz L, Shiu M, Tang E, Sawalha J, Greenshaw A, Rhind S, Fang X, Norbash A, Jetly R, Vermetten E, Brémault-Phillips S. 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 2020;9(10):e20620 View
  3. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  4. Reyes A, Serafica R, Sojobi A. College student veterans' experience with a mindfulness- and acceptance-based mobile app intervention for PTSD: A qualitative study. Archives of Psychiatric Nursing 2020;34(6):497 View
  5. Worthington M, Mandavia A, Richardson-Vejlgaard R. Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning. BMC Psychiatry 2020;20(1) View
  6. Schultebraucks K, Sijbrandij M, Galatzer-Levy I, Mouthaan J, Olff M, van Zuiden M. 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 2021;14:100297 View
  7. Wani A, Aiello A, Kim G, Xue F, Martin C, Ratanatharathorn A, Qu A, Koenen K, Galea S, Wildman D, Uddin M. 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 2021;282:894 View
  8. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  9. Baumeister H, Bauereiss N, Zarski A, Braun L, Buntrock C, Hoherz C, Idrees A, Kraft R, Meyer P, Nguyen T, Pryss R, Reichert M, Sextl T, Steinhoff M, Stenzel L, Steubl L, Terhorst Y, Titzler I, Ebert D. 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 2021;12 View
  10. Warner E, Nannarone M, Manuel D, Lashewicz B, Patten S, Schmitz N, Wang J. Self-help behaviors partially mediate the relationship between personalized depression risk disclosure and psychological distress: A mediation analysis using data from a randomized controlled trial. Journal of Psychiatric Research 2021;140:7 View
  11. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View
  12. Shiba K, Daoud A, Kino S, Nishi D, Kondo K, Kawachi I. Uncovering heterogeneous associations of disaster‐related traumatic experiences with subsequent mental health problems: A machine learning approach. Psychiatry and Clinical Neurosciences 2022;76(4):97 View
  13. Rastpour A, McGregor C. Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach. JMIR Mental Health 2022;9(8):e38428 View
  14. Mukherjee S, Rintamaki L, Shucard J, Wei Z, Carlasare L, Sinsky C. A Statistical Learning Approach to Evaluate Factors Associated With Post-Traumatic Stress Symptoms in Physicians: Insights From the COVID-19 Pandemic. IEEE Access 2022;10:114434 View
  15. Mentis A, Lee D, Roussos P. Applications of artificial intelligence−machine learning for detection of stress: a critical overview. Molecular Psychiatry 2023 View
  16. Karstoft K, Eskelund K, Gradus J, Andersen S, Nissen L. Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models. Journal of Psychiatric Research 2023;163:109 View
  17. Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Frontiers in Psychiatry 2023;14 View
  18. Aich K, Kashyap S, Tyagi K, Verma I, Chauhan A, Jain C. Understanding the Potentiality of Artificial Intelligence in Psychological Disorders Detection and Diagnostics. OBM Neurobiology 2023;07(04):1 View
  19. Singh A, Gupta S, Goel L, Agarwal A, Dargar S. Archimedes optimization-based Elman Recurrent Neural Network for detection of post-traumatic stress disorder. Biomedical Signal Processing and Control 2024;90:105806 View
  20. Lee S, Kim J. Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling. Frontiers in Psychology 2024;14 View
  21. Lamb R, Firestone J, Kavner A, Almusharraf N, Choi I, Owens T, Rodrigues H. Machine learning prediction of mental health strategy selection in school aged children using neurocognitive data. Computers in Human Behavior 2024;156:108197 View
  22. Wójcik Z, Dimitrova V, Warrington L, Velikova G, Absolom K. Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures. JCO Clinical Cancer Informatics 2024;(8) View
  23. Horwitz A, McCarthy K, House S, Beaudoin F, An X, Neylan T, Clifford G, Linnstaedt S, Germine L, Rauch S, Haran J, Storrow A, Lewandowski C, Musey Jr. P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Hudak L, Pascual J, Seamon M, Harris E, Pearson C, Peak D, Domeier R, Rathlev N, Sergot P, Sanchez L, Bruce S, Joormann J, Harte S, Koenen K, McLean S, Sen S. Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning. Journal of Anxiety Disorders 2024;104:102876 View
  24. Wang J, Ouyang H, Jiao R, Cheng S, Zhang H, Shang Z, Jia Y, Yan W, Wu L, Liu W. The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis. npj Digital Medicine 2024;7(1) View
  25. Razavi M, Ziyadidegan S, Jahromi R, Kazeminasab S, Baharlouei E, Janfaza V, Mahmoudzadeh A, Sasangohar F. Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review (Preprint). JMIR Mental Health 2023 View

Books/Policy Documents

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