Big data and machine learning in predicting suicidal ideation: an integrative review
DOI:
https://doi.org/10.26694/reufpi.v14i1.6249Keywords:
Big Data, Machine Learning, Suicidal Ideation, Mental HealthAbstract
Objective: To analyze, in literature, the application of Machine Learning and Big Data techniques to predict suicidal ideation in different populations. Methods: An integrative review, conducted according to the model proposed by Whittemore and Knafl in October 2024. Two independent reviewers performed the search in the MEDLINE/PubMed, Web of Science and PsycINFO databases. Primary studies that addressed the use of Big Data and Machine Learning in predicting suicidal ideation were included, with no restrictions regarding language or publication date. The presentation of results followed the PRISMA protocol guidelines. Results: Ten studies were selected to compose this review, which demonstrated the potential of Big Data and Machine Learning in identifying risk patterns for suicidal ideation in various groups, such as sexual and gender minorities, students, psychiatric patients, patients who have suffered a stroke, workers and the general population, with high predictive accuracy. Conducted in Asia and North America, the studies employed varied collection and analysis methods, with random forest standing out as a recurring and potentially effective technique. Conclusion: The use of Big Data and Machine Learning in mental health offers significant advances in predicting suicidal ideation.
References
Ministério da Saúde (BR). Suicídio (Prevenção). Brasília: Ministério da Saúde; 2024. Available from: https://www.gov.br/saude/pt-br/assuntos/saude-de-a-a-z/s/suicidio-prevencao.
Santos J, Pimentel FO, Méa CPD, Patias ND. Ideação suicida na adolescência e fatores associados. Arquivos Brasileiros de Psicologia. 2022; 74:e024. Doi:10.36482/1809-5267.ARBP-2022v74.19801
World Health Organization. Suicide worldwide in 2019: global health estimates. Geneva: World Health Organization; 2021. ISBN: 9789240026643.
Alves FJO, Fialho E, Araújo JAP, Naslund JA, Barreto ML, Patel V, Machado DB. The rising trends of self-harm in Brazil: an ecological analysis of notifications, hospitalisations, and mortality between 2011 and 2022. Lancet Reg Health Am. 2024;31:100691. DOI: 10.1016/j.lana.2024.100691.
Ministério da Saúde (BR). Boletim epidemiológico. Panorama dos suicídios e lesões autoprovocadas no Brasil de 2010 a 2021. Brasília: Ministério da Saúde, Secretaria de Vigilância em Saúde e Ambiente; 2024. Available from: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/boletins/epidemiologicos/edicoes/2024/boletim-epidemiologico-volume-55-no-04.pdf.
Barbosa SS, Rodrigues J, Guimarães GF, Lopes SMB. Aplicativos de celular na prevenção do comportamento suicida. SMAD, Rev Eletrônica Saúde Mental Álcool Drog. 2020;16(4):100-8. Doi: 10.11606/issn.1806-6976.smad.2020.167062
Oliveira LM de, Fernandes Junior LCC. Aplicabilidade da inteligência artificial na psiquiatria: uma revisão de ensaios clínicos. Debates em Psiquiatria [Internet]. 2020;10(1):14-25. DOI: 10.25118/2236-918X-10-1-2
Whittemore R, Knafl K. The integrative review: updated methodology. J Adv Nurs. 2005;52(5):546-553. Doi:10.1111/j.1365-2648.2005.03621.x
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 202;372(71). DOI: 10.1136/bmj.n71
Lei C, Qu D, Liu K, Chen R. Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals. JAMA Netw Open. 2023;6(9):e2333164. doi:10.1001/jamanetworkopen.2023.33164
Shin S, Kim K. Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death. Asian J Psychiatr. 2023;88:103725. doi:10.1016/j.ajp.2023.103725
Kim S, Lee K. The Effectiveness of Predicting Suicidal Ideation through Depressive Symptoms and Social Isolation Using Machine Learning Techniques. J Pers Med. 2022;12(4):516. Published 2022 Mar 22. doi:10.3390/jpm12040516
Bozzay ML, Hughes CD, Eickhoff C, Schatten H, Armey MF. Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide. J Affect Disord. 2024;364:57-64. doi:10.1016/j.jad.2024.08.038
Li TMH, Chen J, Law FOC, Li CT, Chan NY, Chan JWY, et al. Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study. JMIR Med Inform. 2023;11:e50221. doi:10.2196/50221
Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med. 2020;3:78. Published 2020 May 26. doi:10.1038/s41746-020-0287-6
Hwanjin P, Lee K. A Machine Learning Approach for Predicting Wage Workers' Suicidal Ideation. J Pers Med. 2022;12(6):945. doi:10.3390/jpm12060945
Lee J, Pak TY. Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study. SSM Popul Health. 2022;19:101231. doi:10.1016/j.ssmph.2022.101231
Lekkas D, Klein RJ, Jacobson NC. Predicting acute suicidal ideation on Instagram using ensemble machine learning models. Internet Interv. 2021;25:100424. doi:10.1016/j.invent.2021.100424
Song SI, Hong HT, Lee C, Lee SB. A machine learning approach for predicting suicidal ideation in post stroke patients. Sci Rep. 2022;12(1):15906. doi:10.1038/s41598-022-19828-8
Couronné R, Probst P, Boulesteix AL. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics. 2018;19(1):270. doi: 10.1186/s12859-018-2264-5.
Ignatenko V, Surkov A, Koltcov S. Random forests with parametric entropy-based information gains for classification and regression problems. PeerJ Comput Sci. 2024 Jan 3;10:e1775. doi: 10.7717/peerj-cs.1775.
Gremsl T, Hödl E. Emotional AI: legal and ethical challenges. Information Polity. 2022;27(2):163-174.
Waring J, Lindvall C, Umeton R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif Intell Med. 2020 Apr;104:101822. doi: 10.1016/j.artmed.2020.101822.
Habehh H, Gohel S. Machine Learning in Healthcare. Curr Genomics. 2021 Dec 16;22(4):291-300. doi: 10.2174/1389202922666210705124359.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rev Enferm UFPI

This work is licensed under a Creative Commons Attribution 4.0 International License.
Autores mantém os direitos autorais e concedem à REUFPI o direito de primeira publicação, com o trabalho licenciado sob a Licença Creative Commons Attibution BY 4.0 que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.