Factors and predictors of length of stay in offenders diagnosed with schizophrenia- A machine-learning-based approach

Abstract

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.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…We identified 26 studies regarding the prediction of the hospital length of stay that used data science methods. Twenty-three studies used a retrospective cohort design, 126 133 159 173 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 while three were prospective cohort studies. 129 195 196 Data sources mostly used administrative databases 126 133 179 180 182 186 191 192 194 and EHRs, 129 133 176 183 184 188 190 192 196 while other studies used publicly available datasets, 159 173 178 187 189 data warehouses and registries, 133 177 180 195 paper clinical notes, 193 paper patient records, 185 research electronic data capture systems, 188 trial datasets, 181 questionnaires, 196 and routine bedside monitors.…”

“…Study populations included surgical patients, 133 159 177 179 181 182 183 195 196 ICU patients, 173 176 178 187 189 190 medical-surgical patients, 126 129 180 191 patients presenting to the ED, 184 188 193 194 and psychiatric patients. 185 186 192 Most studies were conducted using U.S. patient data, 129 133 159 173 176 177 178 181 182 183 187 189 191 while other studies used patient data from Australia, 126 179 193 194 Brazil, 186 188 Canada, 195 196 China, 180 England, 190 Germany, 192 Switzerland, 185 and Taiwan. 184…”

“…126 133 159 177 178 181 182 183 186 188 190 191 192 195 Other predictors used clinical data, 126 176 177 178 180 181 184 185 187 190 194 195 laboratory tests, 126 129 133 173 178 181 182 187 189 190 vital signs, 126 129 133 173 176 178 184 187 189 hospitalization data (e.g., admission/discharge data and hospital characteristics), 126 129 133 159 186 191 192 194 surgery data, 133 177 179 181 182 195 196 anthropometric data, 159 177 178 181 184 187 195 scales/instruments, 126 180 188 …”