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

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

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 coe...

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Bibliographic Details
Main Authors: Florian Hotzy, Anastasia Theodoridou, Paul Hoff, Andres R. Schneeberger, Erich Seifritz, Sebastian Olbrich, Matthias Jäger
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-06-01
Series:Frontiers in Psychiatry
Subjects:
coercion
seclusion
restraint
coercive medication
involuntary hospitalization
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fpsyt.2018.00258/full

Internet

https://www.frontiersin.org/article/10.3389/fpsyt.2018.00258/full

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