No abstract

This study employs machine learning algorithms to examine the causes for engaging in violent offending in individuals with schizophrenia spectrum disorders. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy, Zurich University Hospital of Psychiatry, Switzerland. Based on findings of the general strain theory and using logistic regression and machine learning algorithms, it was analyzed whether accumulation and type of stressors in the inpatients’ history influenced the severity of an offense. A higher number of stressors led to more violent offenses, and five types of stressors were identified as being highly influential regarding violent offenses. Our findings suggest that an accumulation of stressful experiences in the course of life and certain types of stressors might be particularly important in the development of violent offending in individuals suffering from schizophrenia spectrum disorders. A better understanding of risk factors that lead to violent offenses should be helpful for the development of preventive and therapeutic strategies for patients at risk and could thus potentially reduce the prevalence of violent offenses.

The link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors contributing to completed homicides committed by offenders diagnosed with schizophrenia referred to a Swiss forensic institution, using machine learning algorithms. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. A total of 519 variables were explored to differentiate homicidal and other (violent and non-violent) offenders. The dataset was split employing variable filtering, model building, and selection embedded in a nested resampling approach. Ten factors regarding criminal and psychiatric history and clinical factors were identified to be influential in differentiating between homicidal and other offenders. Findings expand the research on influential factors for completed homicide in patients with schizophrenia. Limitations, clinical relevance, and future directions are discussed.

Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients' characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.