Machine learning

Do patterns and types of negative affect during hospitalization predict short‐term post‐discharge suicidal thoughts and behaviors?

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

A pilot study using frequent inpatient assessments of suicidal thinking to predict short-term postdischarge suicidal behavior

Machine learning enhances prediction of illness course: A longitudinal study in eating disorders

Ethical dilemmas created by mobile health and machine learning within research in psychiatry

Longitudinal predictors of self-injurious thoughts and behaviors in sexual and gender minority adolescents

Model Complexity Improves the Prediction of Nonsuicidal Self-Injury

Cognitive rigidity and heightened attention to detail occur transdiagnostically in adolescents with eating disorders

Machine Learning

Meta-analyses indicate that efforts to predict self-harming behaviors have resulted in near-chance accuracy. Prediction of these behaviors may benefit from machine learning methods, which can better account for the complexity of self-harming behaviors. My work in this area uses machine learning to improve the longitudinal prediction of nonsuicidal self-injury (NSSI), suicide, and eating disorders across both short-term periods of high risk and long-term studies of illness course and outcome.