Machine learning models for temporally precise lapse prediction in alcohol use disorder.

ABSTRACT: We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw scores and longitudinal change in theoretically implicated risk factors collected through ecological momentary assessment. Participants (N = 151, 51% male, M_age = 41, 87% White, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) from alcohol use disorder provided 4 × daily ecological momentary assessment for up to 3 months. We used grouped, nested cross-validation to select the best models and evaluate the performance of those best models. Models yielded median areas under the receiver operating curves of 0.89,

SUBMITTER: Wyant K 

PROVIDER: S-EPMC11556439 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature