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Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Author

Listed:
  • Rebecca A. Bernert

    (Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA)

  • Amanda M. Hilberg

    (Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA)

  • Ruth Melia

    (Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
    Department of Psychology, National University of Ireland, Galway, Ireland)

  • Jane Paik Kim

    (Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA)

  • Nigam H. Shah

    (Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94304, USA
    Informatics, Stanford Center for Clinical and Translational Research, and Education (Spectrum), Stanford University, Stanford CA 94304, USA
    Indicates Co-Senior Authorship.)

  • Freddy Abnousi

    (Facebook, Menlo Park, CA 94025, USA
    Yale University School of Medicine, New Haven, CT 06510, USA
    Indicates Co-Senior Authorship.)

Abstract

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.

Suggested Citation

  • Rebecca A. Bernert & Amanda M. Hilberg & Ruth Melia & Jane Paik Kim & Nigam H. Shah & Freddy Abnousi, 2020. "Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations," IJERPH, MDPI, vol. 17(16), pages 1-25, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5929-:d:399405
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    References listed on IDEAS

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    Cited by:

    1. Carl B. Roth & Andreas Papassotiropoulos & Annette B. Brühl & Undine E. Lang & Christian G. Huber, 2021. "Psychiatry in the Digital Age: A Blessing or a Curse?," IJERPH, MDPI, vol. 18(16), pages 1-32, August.
    2. José Eduardo Rodríguez-Otero & Xiana Campos-Mouriño & David Meilán-Fernández & Sarai Pintos-Bailón & Graciela Cabo-Escribano, 2022. "Where is the social in the biopsychosocial model of suicide prevention?," International Journal of Social Psychiatry, , vol. 68(7), pages 1403-1410, November.
    3. Abayomi Arowosegbe & Tope Oyelade, 2023. "Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review," IJERPH, MDPI, vol. 20(2), pages 1-23, January.

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