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Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD

Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcome...

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Autores principales: Machetanz, Lena, Huber, David, Lau, Steffen, Kirchebner, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600890/
https://www.ncbi.nlm.nih.gov/pubmed/36292198
http://dx.doi.org/10.3390/diagnostics12102509
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author Machetanz, Lena
Huber, David
Lau, Steffen
Kirchebner, Johannes
author_facet Machetanz, Lena
Huber, David
Lau, Steffen
Kirchebner, Johannes
author_sort Machetanz, Lena
collection PubMed
description Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors.
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spelling pubmed-96008902022-10-27 Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD Machetanz, Lena Huber, David Lau, Steffen Kirchebner, Johannes Diagnostics (Basel) Article Today’s extensive availability of medical data enables the development of predictive models, but this requires suitable statistical methods, such as machine learning (ML). Especially in forensic psychiatry, a complex and cost-intensive field with risk assessments and predictions of treatment outcomes as central tasks, there is a need for such predictive tools, for example, to anticipate complex treatment courses and to be able to offer appropriate therapy on an individualized basis. This study aimed to develop a first basic model for the anticipation of adverse treatment courses based on prior compulsory admission and/or conviction as simple and easily objectifiable parameters in offender patients with a schizophrenia spectrum disorder (SSD). With a balanced accuracy of 67% and an AUC of 0.72, gradient boosting proved to be the optimal ML algorithm. Antisocial behavior, physical violence against staff, rule breaking, hyperactivity, delusions of grandeur, fewer feelings of guilt, the need for compulsory isolation, cannabis abuse/dependence, a higher dose of antipsychotics (measured by the olanzapine half-life) and an unfavorable legal prognosis emerged as the ten most influential variables out of a dataset with 209 parameters. Our findings could demonstrate an example of the use of ML in the development of an easy-to-use predictive model based on few objectifiable factors. MDPI 2022-10-16 /pmc/articles/PMC9600890/ /pubmed/36292198 http://dx.doi.org/10.3390/diagnostics12102509 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Machetanz, Lena
Huber, David
Lau, Steffen
Kirchebner, Johannes
Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title_full Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title_fullStr Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title_full_unstemmed Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title_short Model Building in Forensic Psychiatry: A Machine Learning Approach to Screening Offender Patients with SSD
title_sort model building in forensic psychiatry: a machine learning approach to screening offender patients with ssd
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600890/
https://www.ncbi.nlm.nih.gov/pubmed/36292198
http://dx.doi.org/10.3390/diagnostics12102509
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