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

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

Books/Policy Documents

  1. O’Leary B, Shih C, Chen T, Xie H, Cotton A, Xu K, Morey R, Wang X. Brain Informatics. View