Machine learning models may have greater accuracy than gold-standard risk assessment tools for predicting criminal offense among people with psychiatric disorders, according to study results published in Journal of Psychiatric Research. "Knowing the type of crime an individual is likely to commit, before the offense occurs, is urgently needed in order to guide more targeted and precise risk assessment strategies and frontline therapeutic interventions," Devon Watts, of the department of psychiatry and behavioral neurosciences at McMaster University in Canada, and colleagues wrote. "Furthermore, the vast majority of work thus far has focused on predicting recidivism in non-psychiatric prison populations. Importantly, it is largely unclear whether such models can be appropriately extrapolated to offenses committed by those with severe mental illness." Current, actuarial risk estimates are unable to individually predict criminal offense type a patient will go on to commit, and they frequently simply evaluate the general risk for crime occurring among a group sample, according to the researchers. In the current study, Watts and colleagues sought to create a machine learning model able to predict criminal offense type committed among a large transdiagnostic sample of psychiatry patients, on the individual level.