LESS-IS-MORE: LESS INFORMATION CAN IMPROVE CLINICAL RISK ASSESSMENT JUDGEMENTS
Method(s): We specifically looked at the problem of assessing the risk of violence among clinical populations in a forensic psychiatric setting. We tested different models predicting violent behaviour - a simple tallying model that just sums up the evidence across all items without performing any differential weighting of the items, and a regularised regression model which can select the items which are more important than others and weigh them accordingly. We trained both models on a dataset of the HCR–20 (Historical, Clinical, and Risk Management) violence risk assessment scores and basic demographic information about the patients such as age and gender, together with a connected patient record about violent behaviour from 366 patients at a major medium-security hospital in the UK. We trained each model on 1) all available HCR-20 items and 2) only half of the HCR-20 items (10) which were selected such as to optimise each model’s out-of-sample performance.
Result(s): We found that the tallying model which only uses unit weights on the items (and has a cutoff threshold as a parameter) exhibits the same level of predictive accuracy as the more complex regression model. Moreover, we find that surprisingly, training the models on 10 predictors instead of 20 resulted in superior performance for the simple tallying model and the regularised regression model.
Conclusion(s): Our findings show that the simple tallying model performed surprisingly well in comparison to regularised regression. However, the performance of both of these models when trained on only half of the HCR-20 items, suggesting that the HCR-20 instrument can be improved by feeding it less information. This can potentially save costs and time of risk assessments, while improving diagnostic judgments, thus benefitting clinicians and patients.