Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study

Abstract

Background Predicting which treatment will work for which patient in mental health care remains a challenge. Objective The aim of this multisite study was 2-fold: (1) to predict patients’ response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models. Methods Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites. Results The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80. Conclusions Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

“…Collaborative efforts between researchers, healthcare providers, and data scientists are imperative to harness the full potential of predictive modeling in advancing patient-centered care and improving healthcare quality in the years ahead. Lastly, as machine learning techniques are leveraged to predict outcomes, recommend therapies, and identify high-risk patients, understanding the rationale behind these predictions becomes paramount [84]. Explainable AI methods offer a means to elucidate the decision-making process of complex models, providing healthcare professionals with insights into the features and patterns contributing to personalized recommendations.…”