An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.

Digital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms.