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

Depression and Mood Disorders; Suicide Prevention (1029) Clinical Information and Decision Making (902) New Methods (665) Methods and Feasibility Studies (284) Decision Support for Health Professionals (736) Electronic Health Records (782) Diagnostic Tools in Mental Health (171) Machine Learning (1004) Special Issue on Computing and Mental Health (2018) (32)

Published on in Vol 5 , No 2 (2018) :Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10144, first published .
Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study

Authors of this article:

Marcos DelPozo-Banos 1 Author Orcid Image ;   Ann John 1 Author Orcid Image ;   Nicolai Petkov 2 Author Orcid Image ;   Damon Mark Berridge 1 Author Orcid Image ;   Kate Southern 3 Author Orcid Image ;   Keith LLoyd 1 Author Orcid Image ;   Caroline Jones 4 Author Orcid Image ;   Sarah Spencer 5 Author Orcid Image ;   Carlos Manuel Travieso 6 Author Orcid Image
Article Authors Cited by (24) Tweetations (71) Metrics

Journals

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  16. Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. Adolescent Psychiatry 2022;12(1):1 View
  17. Lara-González L, Delgado-Luna M, De León-Galván B, Venegas-Guerrero J. Comparison of Machine Learning algorithms for the Burnout projection. ECORFAN Journal-Democratic Republic of Congo 2021:1 View
  18. Gupta M, Gupta N, Robinson M. A panorama of the medicolegal aspects of suicide assessments: integrating multiple vantage points in improving quality, safety, and risk management. CNS Spectrums 2022:1 View
  19. Kirtley O, van Mens K, Hoogendoorn M, Kapur N, de Beurs D. Translating promise into practice: a review of machine learning in suicide research and prevention. The Lancet Psychiatry 2022;9(3):243 View
  20. Chen S, Huang H, Liu S, Chen S. Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm. OMEGA - Journal of Death and Dying 2021:003022282110605 View
  21. Balbuena L, Baetz M, Sexton J, Harder D, Feng C, Boctor K, LaPointe C, Letwiniuk E, Shamloo A, Ishwaran H, John A, Brantsæter A. Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning. BMC Psychiatry 2022;22(1) View
  22. Rees S, Fry R, Davies J, John A, Condon L, Page K. Can routine data be used to estimate the mental health service use of children and young people living on Gypsy and Traveller sites in Wales? A feasibility study. PLOS ONE 2023;18(2):e0281504 View
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Books/Policy Documents

  1. Martinez-Ales G, Hernandez-Calle D, Khauli N, Keyes K. Behavioral Neurobiology of Suicide and Self Harm. View