Disparities in adherence and emergency department utilization among people with epilepsy: A machine learning approach.

ABSTRACT:

Purpose

We used a machine learning approach to identify the combinations of factors that contribute to lower adherence and high emergency department (ED) utilization.

Methods

Using Medicaid claims, we identified adherence to anti-seizure medications and the number of ED visits for people with epilepsy in a 2-year follow up period. We used three years of baseline data to identify demographics, disease severity and management, comorbidities, and county-level social factors. Using Classification and Regression Tree (CART) and random forest analyses we identified combinations of baseline factors that predicted lower adherence and ED visits. We further stratified these models by race and ethnicity.

Results

From 52,175 people with epilepsy, the CART model identified develop

SUBMITTER: Bensken WP 

PROVIDER: S-EPMC10528555 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature