Using random forests to model 90-day hometime in people with stroke.

ABSTRACT:

Background

Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult. In this proof-of-concept study we evaluated the performance of random forests regression in the analysis of hometime.

Methods

Using administrative data we identified stroke hospitalizations between 2010 and 2017 in Ontario, Canada. We used random forests regression to predict 90-day hometime using 15 covariates. Model accuracy was determined using the r-squared statistic. Variable importance in prediction and the marginal effects of each covariate were explored.

Results

We identified 75,745 eligible patients. Median 90-day hometime was 59 d

SUBMITTER: Holodinsky JK 

PROVIDER: S-EPMC8112132 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature