Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach

Abstract

Objective: Patterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to generate personalized predictions of symptom trajectories among patients receiving treatment for EDs, and compared model performance to a simpler logistic regression prediction model.

Method: Participants were adolescent girls and adult women (N = 333) presenting for residential ED treatment. Self-report progress assessments were completed at admission, discharge, and weekly throughout treatment. Latent growth mixture modeling previously identified three latent treatment response trajectories (Rapid Response, Gradual Response, and Low-Symptom Static Response) and assigned a trajectory type to each patient. Machine learning models (support vector, k-nearest neighbors) and logistic regression were applied to these data to predict a patient's response trajectory using data from the first 2 weeks of treatment.

Results: The best-performing machine learning model (evaluated via area under the receiver operating characteristics curve [AUC]) was the radial-kernel support vector machine (AUC_RADIAL = 0.94). However, the more computationally-intensive machine learning models did not improve predictive power beyond that achieved by logistic regression (AUC_LOGIT = 0.93). Logistic regression significantly improved upon chance prediction (M_AUC[NULL] = 0.50, SD = .01; p <.001).

Discussion: Prediction of ED treatment response trajectories is feasible and achieves excellent performance, however, machine learning added little benefit. We discuss the need to explore how advance knowledge of expected trajectories may be used to plan treatment and deliver individualized interventions to maximize treatment effects.

Keywords: feasibility studies; feeding and eating disorders; health services research; machine learning; precision medicine; statistical methodology; support vector machine; treatment outcome.