Identifying Direct Coercion in a High Risk Subgroup of Offender Patients With Schizophrenia via Machine Learning Algorithms

This study aims to explore risk factors for direct coercive measures (seclusion, restraint, involuntary medication) in a high risk subpopulation of offender patients with schizophrenia spectrum disorders. Five hundred sixty nine potential predictor variables were explored in terms of their predictive power for coercion/no coercion in a set of 131 (36.6%) offender patients who experienced coercion and 227 who did not, using machine learning analysis. The dataset was split (70/30%) applying variable filtering, machine learning model building, and selection embedded in nested resampling approach in one subset. The best model was then selected, and the most important variables extracted on the second data subset. In the final model the following variables identified coercion with a balanced accuracy of 73.28% and a predictive power (area under the curve, AUC) of 0.8468: threat of violence, (actual) violence toward others, the application of direct coercive measures during past psychiatric inpatient treatments, the positive and negative syndrome scales (PANSS) poor impulse control, uncooperativeness, and hostility and the total PANSS-score at admission, prescription of haloperidol during inpatient treatment, the daily cumulative olanzapine equivalent antipsychotic dosage at discharge, and the legal prognosis estimated by a team of licensed forensic psychiatrists. Results confirm prior findings, add detail on factors indicative for the use of direct coercion, and provide clarification on inconsistencies. Limitations, clinical relevance, and avenues for future research are discussed.