Predicting treatment response using machine learning: A registered report
| dc.date.accessioned | 2024-05-25T07:42:43Z | |
| dc.date.available | 2024-05-25T07:42:43Z | |
| dc.date.issued | 2024 | |
| dc.identifier | doi:10.17170/kobra-2024052410204 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/15779 | |
| dc.description.sponsorship | Gefördert im Rahmen des Projekts DEAL | |
| dc.language.iso | eng | |
| dc.rights | Namensnennung 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | inpatients | eng |
| dc.subject | machine learning | eng |
| dc.subject | predictive modelling | eng |
| dc.subject | prognostic markers | eng |
| dc.subject | treatment response | eng |
| dc.subject.ddc | 150 | |
| dc.title | Predicting treatment response using machine learning: A registered report | eng |
| dc.type | Aufsatz | |
| dcterms.abstract | Objective: - Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively screening for patients at risk of low treatment response, gain more knowledge about risk factors and to evaluate treatments, accurate insights about predictors of treatment response in naturalistic inpatient samples are needed. // Methods: We compared the performance of different machine learning algorithms in predicting treatment response, operationalized as a substantial reduction in symptom severity as expressed in the Patient Health Questionnaire Anxiety and Depression Scale. To achieve this goal, we used different sets of variables—(a) demographics, (b) physical indicators, (c) psychological indicators and (d) treatment-related variables—in a naturalistic inpatient sample (N = 723) to specify their joint and unique contribution to treatment success. // Results: There was a strong link between symptom severity at baseline and post-treatment (R² = .32). When using all available variables, both machine learning algorithms outperformed the linear regressions and led to an increment in predictive performance of R² = .12. Treatment-related variables were the most predictive, followed psychological indicators. Physical indicators and demographics were negligible. // Conclusions: Treatment response in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators. Regularization via machine learning algorithms leads to higher predictive performances as opposed to including nonlinear and interaction effects. Heterogenous aspects of mental health have incremental predictive value and should be considered as prognostic markers when modelling treatment processes. | eng |
| dcterms.accessRights | open access | |
| dcterms.creator | Jankowsky, Kristin | |
| dcterms.creator | Krakau, Lina | |
| dcterms.creator | Schroeders, Ulrich | |
| dcterms.creator | Zwerenz, Rüdiger | |
| dcterms.creator | Beutel, Manfred E. | |
| dc.relation.doi | doi:10.1111/bjc.12452 | |
| dc.subject.swd | Maschinelles Lernen | ger |
| dc.subject.swd | Stationäre Behandlung | ger |
| dc.subject.swd | Psychotherapie | ger |
| dc.type.version | publishedVersion | |
| dcterms.source.identifier | eissn:2044-8260 | |
| dcterms.source.issue | Issue 2 | |
| dcterms.source.journal | The British Journal of Clinical Psychology | eng |
| dcterms.source.pageinfo | 137-155 | |
| dcterms.source.volume | Volume 63 | |
| kup.iskup | false | |
| ubks.epflicht | true |
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