Machine Learning
An Approach in Identifying Risk Factors for Coercion Compared to Binary Logistic Regression
Bibliographic Data
| ID | 15525577 |
|---|---|
| Authors | Florian Hotzy (0000-0003-0661-9608, University Hospital of Zurich, corresponding author), Anastasia Theodoridou (0000-0003-4792-385X, University Hospital of Zurich), Paul Hoff (0000-0002-9854-414X, University Hospital of Zurich), Andres Ricardo Schneeberger (0000-0001-8176-9126, Universitäre Psychiatrische Dienste Bern), Erich Seifritz (0000-0002-7311-4426, University Hospital of Zurich), Sebastian Olbrich (0000-0001-5557-4878, University Hospital of Zurich), Matthias Jäger (0000-0003-1059-3949, University Hospital of Zurich) |
| Year | 2018 |
| Volume | 9 |
| Pages | 258-258 |
| Publication date | 2018-06-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2018.00258 |
| PMID | 29946273 |
| OpenAlex | W2808486778 |
| Language | EN |
| Citations received | 5 |
| References cited | 4 |
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
Coercion (linguistics · Logistic regression · Machine learning · Clinical Psychology · Computer Science · Emergency and Acute Care Studies · Healthcare Decision-Making and Restraints · Psychology · Psychopathy, Forensic Psychiatry, Sexual Offending · Artificial Intelligence
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| Unique citing works | 5 |
|---|---|
| Citations per year | 0,83 |
| Citation span | 2020 - 2025 (6) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |