Aufsatz
Predicting treatment response using machine learning: A registered report
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.
- 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.
Citation
In: The British Journal of Clinical Psychology Volume 63 / Issue 2 (2024) , S. 137-155 ; eissn:2044-8260Sponsorship
Gefördert im Rahmen des Projekts DEALCitation
@article{doi:10.17170/kobra-2024052410204,
author={Jankowsky, Kristin and Krakau, Lina and Schroeders, Ulrich and Zwerenz, Rüdiger and Beutel, Manfred E.},
title={Predicting treatment response using machine learning: A registered report},
journal={The British Journal of Clinical Psychology},
year={2024}
}
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2024-05-25T07:42:43Z 2024-05-25T07:42:43Z 2024 doi:10.17170/kobra-2024052410204 http://hdl.handle.net/123456789/15779 Gefördert im Rahmen des Projekts DEAL eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ inpatients machine learning predictive modelling prognostic markers treatment response 150 Predicting treatment response using machine learning: A registered report Aufsatz 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. open access Jankowsky, Kristin Krakau, Lina Schroeders, Ulrich Zwerenz, Rüdiger Beutel, Manfred E. doi:10.1111/bjc.12452 Maschinelles Lernen Stationäre Behandlung Psychotherapie publishedVersion eissn:2044-8260 Issue 2 The British Journal of Clinical Psychology 137-155 Volume 63 false true
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