Sunday, 30 August 2015

Using Bayesian networks to assess and manage risk of violent reoffending among prisoners

Fragment of BN model
Probation officers, clinicians, and forensic medical practitioners have for several years sought improved decision support for determining whether and when to release prisoners with mental health problems and a history of violence.  It is critical that the risk of violent re-offending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established 'risk predictors' in this area of research are typically based on statistical regression models and their results are less than convincing. But recent work undertaken at Queen Mary University of London has resulted in Bayesian network (BN) models that not only have much greater accuracy, but which are also much more useful for decision support. The work has been developed as part of a collaboration between the Risk and Information Management group and the medical practitioners of the Violence Prevention Research Unit (VPRU) of the Wolfson Institute of Preventative Medicine.

The (BN) model, called DSVM-P (Decision Support for Violence Management – Prisoners) captures the causal relationships between risk factors, interventions and violence.  It also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. These decision support features are not available in the previous generation of models used by practitioners and forensic psychiatrists.

Full reference:
Constantinou, A., Freestone M., Marsh, W., Fenton, N. E. , Coid, J. (2015) "Risk assessment and risk management of violent reoffending among prisoners", Expert Systems With Applications 42 (21), 7511-7529.  Published version:
Download Pre-publication draft.