Background Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes. Methods Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2. Results Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses. Conclusions ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.
|Original language||English (US)|
|Number of pages||11|
|Early online date||Feb 28 2020|
|State||Published - Jun 2021|
Bibliographical noteFunding Information:
Financial support. This work was supported in part by the McKnight Foundation; National Institute of Mental Health of the National Institutes of Health (Award numbers: K23MH112867, T32MH082761); National Science Foundation (Award number DGE-1745303); Klarman Family Foundation; Hilda and Preston Davis Foundation; and Minnesota Obesity Center. These funding agencies did not influence the design of the study, collection, analysis, and interpretation of data, or writing of the manuscript
Copyright © The Author(s), 2020. Published by Cambridge University Press.
- Anorexia nervosa
- binge-eating disorder
- bulimia nervosa
- computational psychiatry
- eating disorder
- machine learning