Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders

Supervised machine learning demonstrates improved predictive performance for eating disorder disease course outcomes compared to traditional statistical methods, especially in higher-dimensionality settings. These findings support future application of machine learning to complex biopsychosocial datasets to advance precision medicine initiatives in the eating disorder field and better understand the etiology of disease trajectory.