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Binary Logistic Regression

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 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.

Keywords: coercion, seclusion, restraint, coercive medication, involuntary hospitalization, machine learning

 

 

Hotzy et al. Machine Learning and Coercion

INTRODUCTION

The use of coercive measures (e.g., seclusion, physical and mechanical restraint, forced medication) in psychiatric patients is a massive invasion in their integrity and freedom. As a result, the usage of coercion is controversially discussed since the beginning of modern psychiatry and certain approaches have tried to reduce its rates (1). Although some of those approaches were successful, there are still many patients in which coercion is used. Often the usage of coercion seems necessary when the patients are a danger for themselves or for others due to an underlying psychiatric disorder (2, 3). These situations are always associated with an

ethical dilemma. On one side coercion shall help to protect the patient’s or other’s integrity (2, 3). On the other hand it restricts the freedom of the person which is one of the basic human rights (4). Being a threat to oneself or others may have different reasons in psychiatric patients. In some situations patients are delusional and feel threatened by others which leads to the reaction to protect themselves and can result in threats to other patients or staff (5). Also in situations where the patients are threatening themselves or have suicidal ideations caused by the symptoms of their psychiatric disorder, coercive measures might become necessary to secure the patients survival.

The use of coercion distinguishes psychiatry from other medical disciplines where informed patients can decide to accept or reject a specific measure. Psychiatry at one hand aims to help

the patients to develop a self-determined life without burden of psychiatric symptoms. On the other hand psychiatry is legally determined to reject the patients freedom to move (involuntary hospitalization) but also the freedom to reject a specific measure (forced medication, physical or mechanical restraint, seclusion) if harm to self or others has to be disrupted.

It is obvious that such situations are challenging for the patients but also for the therapeutic team. Those challenges were topic of previous studies where it was shown that patients who experienced coercive measures often describe feelings of helplessness (6, 7), fear (8), anger (9, 10) and humiliation

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