ArticleBMC psychiatry2020
Factors and predictors of length of stay in offenders diagnosed with schizophrenia - a machine-learning-based approach.
Article in BMC psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.BMC health services research · 2025Article
- Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Evaluation of severe and chronic factors for extended stays in Japanese medical treatment and supervision act wards.Frontiers in psychiatry · 2025Article
- Factors predicting the length of stay in inpatient forensic psychiatric care in Hungary.Frontiers in psychiatry · 2025Article
- Barriers to discharge: A retrospective study of factors associated with stays of longer than 2 years in a French secure hospital unit.Criminal behaviour and mental health : CBMH · 2024Article
- Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning.International journal of offender therapy and comparative criminology · 2024Article
- Editorial: Applications of artificial intelligence in forensic mental health: opportunities and challenges.Frontiers in psychiatry · 2024Article
- Institutional influence on length of stay in German forensic hospitals: a multilevel analysis of patients with schizophrenia spectrum disorders.Frontiers in psychiatry · 2024Article
- Correlates of Social Isolation in Forensic Psychiatric Patients with Schizophrenia Spectrum Disorders: An Explorative Analysis Using Machine Learning.International journal of environmental research and public health · 2023Article
- Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis.Frontiers in artificial intelligence · 2023Article
- Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD.Diagnostics (Basel, Switzerland) · 2022Article
- Length of stay of hospitalized patients at tertiary psychiatry facilities in Uganda: the role of caregiver's presence.Discover mental health · 2022Article
- A systematic review of the prediction of hospital length of stay: Towards a unified framework.PLOS digital health · 2022Article
- Maintaining social capital in offenders with schizophrenia spectrum disorder-An explorative analysis of influential factors.Frontiers in psychiatry · 2022Article
- Factors Affecting Length of Inpatient Forensic Stay: Retrospective Study From Czechia.Frontiers in psychiatry · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Machine learning in patient flow: a review.Progress in biomedical engineering (Bristol, England) · 2021Review
- Escape and absconding among offenders with schizophrenia spectrum disorder - an explorative analysis of characteristics.BMC psychiatry · 2021Article
- Investigating Length of Stay Patterns and Its Predictors in the South Wales Trauma Network.Advances in rehabilitation science and practiceArticle
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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