Machine learning-based outcome prediction and novel hypotheses generation for substance use disorder treatment.

ABSTRACT:

Objective

Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a novel data analytics approach using machine learning models to discover interaction effects that might be neglected by traditional hypothesis-generating approaches.

Materials and methods

A patient-episode-level substance use treatment discharge dataset and a Federal Bureau of Investigation crime dataset were joined using core-based statistical area codes. Random forests, artificial neural networks, and extreme gradient boosting were applied with a nested cross-validation methodology. Interaction effects were identified based on the ma

SUBMITTER: Nasir M 

PROVIDER: S-EPMC8661425 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature