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Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review

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  • Abayomi Arowosegbe

    (Institute of Health Informatics, University College London, London NW1 2DA, UK
    Division of Informatics, Imaging & Data Sciences, University of Manchester, Manchester M13 9PL, UK)

  • Tope Oyelade

    (Division of Medicine, University College London, London NW3 2PF, UK)

Abstract

(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the detection, diagnosis, and treatment of people. (2) Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. (3) Result: The preliminary search of 5 databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. (4) Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Additionally, to reduce suicides, people with mental problems must be continuously and passively monitored. (5) Conclusions: The use of AI&ML opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review’s analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1514-:d:1035503
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    References listed on IDEAS

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    1. 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.
    2. Nicholas J Carson & Brian Mullin & Maria Jose Sanchez & Frederick Lu & Kelly Yang & Michelle Menezes & Benjamin Lê Cook, 2019. "Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-14, February.
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