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

Preprints (earlier versions) of this paper are available at, 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


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  7. Belsher B, Smolenski D, Pruitt L, Bush N, Beech E, Workman D, Morgan R, Evatt D, Tucker J, Skopp N. Prediction Models for Suicide Attempts and Deaths. JAMA Psychiatry 2019;76(6):642 View
  8. Posada-Quintero H, Molano-Vergara P, Parra-Hernández R, Posada-Quintero J. Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation. Social Sciences 2020;9(3):30 View
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  11. Naghavi A, Teismann T, Asgari Z, Mohebbian M, Mansourian M, Mañanas M. Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region. Diagnostics 2020;10(11):956 View
  12. Grządzielewska M. Using Machine Learning in Burnout Prediction: A Survey. Child and Adolescent Social Work Journal 2021;38(2):175 View
  13. Corke M, Mullin K, Angel-Scott H, Xia S, Large M. Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers. BJPsych Open 2021;7(1) View
  14. D’Hotman D, Loh E. AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health & Care Informatics 2020;27(3):e100175 View
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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
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  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 2023;28(3):282 View
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  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 2024;88(4):1403 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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  25. Abdulazeem H, Whitelaw S, Schauberger G, Klug S, Vathy-Fogarassy Á. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276 View
  26. Somé N, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Frontiers in Psychiatry 2024;15 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
  2. Larsen M, Vo L, Pratap A, Peters D. Tasman’s Psychiatry. View