Machine learning and data-driven models
Machine learning and data-driven models rely on patterns in the data to answer research questions, as compared to relying on researcher-generated hypotheses. These data-driven models show great promise in helping the field predict very difficult-to-predict outcomes, like eating disorder severity and suicide.
Example questions I tackle within this area include:
- How can we best define eating disorder severity?
- Does a better definition of eating disorder severity relate to or improve treatment outcomes?
- How can we better predict eating disorder treatment outcomes?
Example papers within this content area include, but are not limited to:
Forrest, L. N., Ivezaj, V., & Grilo, C. M. (2023). Machine learning versus traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial. Psychological Medicine, 53, 2777–2788.
Forrest, L. N., Jacobucci, R. C., & Grilo, C. M. (2022). Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes. Psychological Medicine, 52, 685–695.
Ortiz, S. N., Forrest, L. N., Ram, S. R., Jacobucci, R. C., & Smith, A. R. (2021). Using shape and weight overvaluation to empirically differentiate severity of other specified feeding or eating disorder. Journal of Affective Disorders, 295, 446–452.