The impact of machine learning in predicting risk of violence: A systematic review

BACKGROUND: Inpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations. METHODS: Following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: We identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data. CONCLUSION: Despite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice. SYSTEMATIC REVIEW REGISTRATION: [www.crd.york.ac.uk/prospero/], identifier [CRD42022310410].