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

ABSTRACT:

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.

SUBMITTER: Haynos AF 

PROVIDER: S-EPMC7483262 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature