Machine learning to advance the prediction, prevention and treatment of eating disorders

Machine learning approaches are just emerging in eating disorders research. Promising early results suggest that such approaches may be a particularly promising and fruitful future direction. However, there are several challenges related to the nature of eating disorders in building robust, reliable and clinically meaningful prediction models. This article aims to provide a brief introduction to machine learning and to discuss several such challenges, including issues of sample size, measurement, imbalanced data and bias; I also provide concrete steps and recommendations for each of these issues. Finally, I outline key outstanding questions and directions for future research in building, testing and implementing machine learning models to advance our prediction, prevention, and treatment of eating disorders. Highlights Machine learning holds significant promise to advance eating disorders research Some key considerations for responsible machine learning application in eating disorders research include issues of sample size, measurement, imbalanced data and bias Future research should prioritize external validation of machine learning models