Key result
Machine learning approaches offer promising directions for eating disorders research, though challenges remain regarding sample size, measurement, imbalanced data, and bias.
Why the study?
Machine learning approaches are emerging in eating disorders research, but several challenges related to the nature of eating disorders hinder building robust, reliable, and clinically meaningful prediction models.
Provides an introduction to machine learning in eating disorders research, discussing challenges and future directions.
May inform ML study design in eating disorders; leaves open rigorous validation in larger, unbiased cohorts before clinical consideration.
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.
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Shirley B. Wang (2021) conducted a review in Eating disorders. Machine learning was evaluated. Machine learning approaches offer promising directions for eating disorders research, though challenges remain regarding sample size, measurement, imbalanced data, and bias.
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