Risk assessment and risk management of violent reoffending among prisoners

Abstract

Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management – Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.