A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders.

We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.