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

Depression and Mood Disorders; Suicide Prevention (1290) Clinical Information and Decision Making (1339) New Methods (911) Methods and Feasibility Studies (369) Decision Support for Health Professionals (1145) Electronic Health Records (971) Diagnostic Tools in Mental Health (224) Machine Learning (1400) Special Issue on Computing and Mental Health (2018) (33)

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-Banos1 Author Orcid Image ;   Ann John1 Author Orcid Image ;   Nicolai Petkov2 Author Orcid Image ;   Damon Mark Berridge1 Author Orcid Image ;   Kate Southern3 Author Orcid Image ;   Keith LLoyd1 Author Orcid Image ;   Caroline Jones4 Author Orcid Image ;   Sarah Spencer5 Author Orcid Image ;   Carlos Manuel Travieso6 Author Orcid Image
Article Authors Cited by (31) Tweetations (63) Metrics

Journals

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  5. Martínez-Alés G, Keyes K. Fatal and Non-fatal Self-Injury in the USA: Critical Review of Current Trends and Innovations in Prevention. Current Psychiatry Reports 2019;21(10) View
  6. John A, DelPozo-Banos M, Gunnell D, Dennis M, Scourfield J, Ford D, Kapur N, Lloyd K. Contacts with primary and secondary healthcare prior to suicide: case–control whole-population-based study using person-level linked routine data in Wales, UK, 2000–2017. The British Journal of Psychiatry 2020;217(6):717 View
  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
  15. Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. Journal of Medical Systems 2020;44(12) View
  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 2023;28(3):282 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 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
  23. Baniadamdizaj S, Baniadamdizaj S. Prediction of Iranian EFL teachers' burnout level using machine learning algorithms and maslach burnout inventory. Iran Journal of Computer Science 2023;6(1):1 View
  24. Zhang J, Liang S, Liu X, Li D, Zhou F, Xiao L, Liu J, Sha S. Factors associated with suicidal attempts in female patients with mood disorder. Frontiers in Public Health 2023;11 View
  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
  27. Pirrolas O, Correia P. Human Resources’ Burnout. Encyclopedia 2024;4(1):488 View
  28. Ehtemam H, Sadeghi Esfahlani S, Sanaei A, Ghaemi M, Hajesmaeel-Gohari S, Rahimisadegh R, Bahaadinbeigy K, Ghasemian F, Shirvani H. Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies. BMC Medical Informatics and Decision Making 2024;24(1) View

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