Risk Factors for Coercion Case Study

ORIGINAL RESEARCH published: 12 June 2018

DOI: 10.3389/fpsyt.2018.00258

Frontiers in Psychiatry | www.frontiersin.org 1 June 2018 | Volume 9 | Article 258

Edited by:

Meichun Mohler-Kuo,

University of Applied Sciences and

Arts of Western Switzerland,

Switzerland

Reviewed by:

Eric Noorthoorn,

GGNet Mental Health Centre,

Netherlands

Raoul Borbé,

Universität Ulm, Germany

*Correspondence:

Florian Hotzy

florian.hotzy@puk.zh.ch

Specialty section:

This article was submitted to

Public Mental Health,

a section of the journal

Frontiers in Psychiatry

Received: 08 November 2017

Accepted: 24 May 2018

Published: 12 June 2018

Citation:

Hotzy F, Theodoridou A, Hoff P,

Schneeberger AR, Seifritz E, Olbrich S

and Jäger M (2018) Machine

Learning: An Approach in Identifying

Risk Factors for Coercion Compared

to Binary Logistic Regression.

Front. Psychiatry 9:258.

DOI: 10.3389/fpsyt.2018.00258

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

Florian Hotzy1*, Anastasia Theodoridou1, Paul Hoff1, Andres R. Schneeberger2,3,4,

Erich Seifritz1, Sebastian Olbrich1 and Matthias Jäger1

1 Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich,

Switzerland, 2 Psychiatrische Dienste Graubuenden, Chur, Switzerland, 3 Universitaere Psychiatrische Kliniken Basel,

Universitaet Basel, Basel, Switzerland, 4 Department of Psychiatry and Behavioral Sciences, Albert Einstein College of

Medicine, New York, NY, United States

Introduction: Although knowledge about the negative effects of coercive measures in

psychiatry exists, its prevalence is still high in clinical routine. This study aimed to define

risk factors and test machine learning algorithms for their accuracy in the prediction of

the risk of being subjected to coercive measures.