Abstract
BackgroundProlonged 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.MethodsIn 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.ResultsTwo 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.ConclusionsIn 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.
Highlights
Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns
The assessment of the hospital’s practitioners is based on a clinical evaluation process, which incorporates the results of established prognosis instruments. The objectives of this exploratory study were to analyse the length of stay using machine learning (1) based on the unique group of forensic offender patients with schizophrenia spectrum disorder, (2) to consider all variables used in previous research on the subject, (3) to identify the most influential of these variables, and (4) to quantify a predictive value to distinguish between long and short stay
Algorithms based solely on the predictor variable “victim injured severely/ fatally” or “index crime: homicide” both resulted in an Area Under the Curve (AUC) of 0.60, which corresponds to 89.55% of the AUC of the algorithm based on all 90 predictor variables
Summary
Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. 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. A recent review of 38 studies in eleven countries summarized a rich set of patient characteristics contributing to length of stay in psychiatric inpatient treatment [6], but concluded that just ten studies were useful in identifying clinically useful predictive factors, since “more rigorous multivariate statistical techniques” are required in order to eliminate confounding factors. Machine learning (ML) is a sub form of artificial intelligence and relies on patterns and inference in a set of data in order to find an algorithm best predicting an outcome (such as length of stay in the present study). In exploratory data analysis it is better suited than conventional statistical methods to uncover previously “invisible” non-linear dependencies between variables, often resulting in better predictive power [23, 24]
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