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Machine Learning: An Approach in Identifying Risk Factors for Coercion Compared to Binary Logistic Regression


Hotzy, Florian; Theodoridou, Anastasia; Hoff, Paul; Schneeberger, Andres R; Seifritz, Erich; Olbrich, Sebastian; Jäger, Matthias (2018). Machine Learning: An Approach in Identifying Risk Factors for Coercion Compared to Binary Logistic Regression. Frontiers in Psychiatry:9:258.

Abstract

Introduction: Although knowledge about negative effects of coercive measures in psychiatry exists, its prevalence is still high in clinical routine. This study aimed at define risk factors and test machine learning algorithms for their accuracy in the prediction of the risk to being subjected to coercive measures. Methods: In a sample of involuntarily hospitalized patients (n = 393) at the University Hospital of Psychiatry Zurich, we analyzed risk factors for the experience of coercion (n = 170 patients) using chi-square tests and Mann Whitney U tests. We trained machine learning algorithms [logistic regression, Supported Vector Machine (SVM), and decision trees] with these risk factors and tested obtained models for their accuracy via five-fold cross validation. To verify the results we compared them to binary logistic regression. Results: In a model with 8 risk-factors which were available at admission, the SVM algorithm identified 102 out of 170 patients, which had experienced coercion and 174 out of 223 patients without coercion (69% accuracy with 60% sensitivity and 78% specificity, AUC 0.74). In a model with 18 risk-factors, available after discharge, the logistic regression algorithm identified 121 out of 170 with and 176 out of 223 without coercion (75% accuracy, 71% sensitivity, and 79% specificity, AUC 0.82). Discussion: Incorporating both clinical and demographic variables can help to estimate the risk of experiencing coercion for psychiatric patients. This study could show that trained machine learning algorithms are comparable to binary logistic regression and can reach a good or even excellent area under the curve (AUC) in the prediction of the outcome coercion/no coercion when cross validation is used. Due to the better generalizability machine learning is a promising approach for further studies, especially when more variables are analyzed. More detailed knowledge about individual risk factors may help to prevent the occurrence of situations involving coercion.

Abstract

Introduction: Although knowledge about negative effects of coercive measures in psychiatry exists, its prevalence is still high in clinical routine. This study aimed at define risk factors and test machine learning algorithms for their accuracy in the prediction of the risk to being subjected to coercive measures. Methods: In a sample of involuntarily hospitalized patients (n = 393) at the University Hospital of Psychiatry Zurich, we analyzed risk factors for the experience of coercion (n = 170 patients) using chi-square tests and Mann Whitney U tests. We trained machine learning algorithms [logistic regression, Supported Vector Machine (SVM), and decision trees] with these risk factors and tested obtained models for their accuracy via five-fold cross validation. To verify the results we compared them to binary logistic regression. Results: In a model with 8 risk-factors which were available at admission, the SVM algorithm identified 102 out of 170 patients, which had experienced coercion and 174 out of 223 patients without coercion (69% accuracy with 60% sensitivity and 78% specificity, AUC 0.74). In a model with 18 risk-factors, available after discharge, the logistic regression algorithm identified 121 out of 170 with and 176 out of 223 without coercion (75% accuracy, 71% sensitivity, and 79% specificity, AUC 0.82). Discussion: Incorporating both clinical and demographic variables can help to estimate the risk of experiencing coercion for psychiatric patients. This study could show that trained machine learning algorithms are comparable to binary logistic regression and can reach a good or even excellent area under the curve (AUC) in the prediction of the outcome coercion/no coercion when cross validation is used. Due to the better generalizability machine learning is a promising approach for further studies, especially when more variables are analyzed. More detailed knowledge about individual risk factors may help to prevent the occurrence of situations involving coercion.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Psychiatry and Mental health
Language:English
Date:12 June 2018
Deposited On:12 Jun 2018 13:14
Last Modified:19 Aug 2018 15:57
Publisher:Frontiers Research Foundation
ISSN:1664-0640
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fpsyt.2018.00258

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