Big data and machine learning in predicting suicidal ideation: an integrative review

Authors

  • Giovanna Vitória Aragão de Almeida Santos Universidade Federal do Piauí
  • Lidya Tolstenko Nogueira Universidade Federal do Piauí
  • Fernando José Guedes da Silva Júnior Universidade Federal do Piauí
  • Belquior Gomes de Aguiar Filho Centro Universitário Uninovafapi
  • Álvaro Sepúlveda Carvalho Rocha Universidade Federal do Piauí
  • Jefferson Abraão Caetano Lira Universidade Federal do Piauí

DOI:

https://doi.org/10.26694/reufpi.v14i1.6249

Keywords:

Big Data, Machine Learning, Suicidal Ideation, Mental Health

Abstract

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.

Author Biographies

Giovanna Vitória Aragão de Almeida Santos, Universidade Federal do Piauí

Universidade Federal do Piauí. Programa de Pós-graduação em Enfermagem. Teresina, Piauí, Brasil.

Lidya Tolstenko Nogueira, Universidade Federal do Piauí

Universidade Federal do Piauí. Programa de Pós-graduação em Enfermagem. Teresina, Piauí, Brasil.

Fernando José Guedes da Silva Júnior, Universidade Federal do Piauí

Universidade Federal do Piauí. Programa de Pós-Graduação em Saúde da Família, da Rede Nordeste de Formação em Saúde da Família. Teresina, Piauí, Brasil.

Belquior Gomes de Aguiar Filho, Centro Universitário Uninovafapi

Acadêmico de Medicina. Centro Universitário Uninovafapi. Teresina, Piauí, Brasil.

Álvaro Sepúlveda Carvalho Rocha, Universidade Federal do Piauí

Universidade Federal do Piauí. Programa de Pós-graduação em Enfermagem. Teresina, Piauí, Brasil.

Jefferson Abraão Caetano Lira, Universidade Federal do Piauí

Universidade Federal do Piauí. Programa de Pós-graduação em Enfermagem. Teresina, Piauí, Brasil.

References

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Published

2025-06-18

How to Cite

1.
Santos GVA de A, Nogueira LT, Silva Júnior FJG da, Aguiar Filho BG de, Rocha Álvaro SC, Lira JAC. Big data and machine learning in predicting suicidal ideation: an integrative review. Rev Enferm UFPI [Internet]. 2025 Jun. 18 [cited 2025 Jul. 8];14(1). Available from: https://www.periodicos.ufpi.br/index.php/reufpi/article/view/6249

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