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Review
. 2022 Feb 15;65(1):1-22.
doi: 10.1192/j.eurpsy.2022.8. Online ahead of print.

Artificial intelligence and suicide prevention: a systematic review

Alban Lejeune  1 , Aziliz Le Glaz  1 , Pierre-Antoine Perron  1 , Johan Sebti  2 , Enrique Baca-Garcia  3 , Michel Walter  1   4 , Christophe Lemey  1   5   4 , Sofian Berrouiguet  1   5   4   6
Affiliations
Review

Artificial intelligence and suicide prevention: a systematic review

Alban Lejeune et al. Eur Psychiatry. .

Abstract

Background: Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide.

Methods: A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords.

Results: Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings.

Conclusions: AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.

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Figures

Figure 1.
Figure 1.
PRISMA flowchart outlining the study selection process.
Figure 2.
Figure 2.
Included studies by country of origin.
Figure 3.
Figure 3.
Number of studies included by year of publication.
Figure 4.
Figure 4.
PRISMA quality assessment of the included studies.
Figure 5.
Figure 5.
Main AI types used. Abbreviations: AI, artificial intelligence; CR, cox regression; DT, decision tree; LR, logistic regression; NN, neural network; RF, random forest; SVM, support vector machine; XGB/GBT, extreme gradient boosting/gradient boosted tree.
Figure 6.
Figure 6.
Performance in AUC of the different algorithms, based on the studies included in Table 1. Abbreviatioins: AUC, area under the curve; BN, Bayesian network; DT, decision tree; LR, logistic regression; NN, neural network; RF, random forest; XGB/GBT, extreme gradient boosting/gradient boosted tree.
See this image and copyright information in PMC

References

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