Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study
Kasper Van Mens1,2, Joran Lokkerbol3, Ben Wijnen4
1Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.
JMIR Medical Informatics
|August 25, 2023
Summary
Machine learning models can predict mental health treatment outcomes using routine data. These models show robust and generalizable performance across different healthcare organizations, aiding clinical practice.
Area of Science:
- Mental Health Care
- Machine Learning Applications
- Clinical Prediction Models
Background:
- Predicting individual treatment efficacy in mental health care is a significant clinical challenge.
- Utilizing routinely collected data for treatment response prediction is an unmet need.
Purpose of the Study:
- To predict patient treatment response in Dutch basic mental health care using routine data.
- To compare machine learning model performance across three Dutch mental health organizations using interpretable models.
Main Methods:
- Applied least absolute shrinkage and selection operator (LASSO) regression to anonymized data from 6452 patients across three organizations.
- Validated models internally using cross-validation and externally on data from other sites.
Main Results:
- Algorithm performance, measured by area under the curve, ranged from 0.77 to 0.80 for both internal and external validations.
- Models demonstrated robust and generalizable performance, with similar outcomes when applied across different sites.
Conclusions:
- Machine learning offers a reliable method for automated risk signaling to identify patients at risk of poor treatment outcomes.
- The study confirms the generalizability of predictive models across different mental health care settings, supporting clinical implementation.
Keywords:
Netherlandsclinical practicedatamachine learningmental healthmodelmodel performanceriskrisk signalingtechnologytreatmenttreatment outcomesMore Related Videos
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