Effective hospital readmission prediction models using machine-learned features.

ABSTRACT:

Background

Hospital readmissions are one of the costliest challenges facing healthcare systems, but conventional models fail to predict readmissions well. Many existing models use exclusively manually-engineered features, which are labor intensive and dataset-specific. Our objective was to develop and evaluate models to predict hospital readmissions using derived features that are automatically generated from longitudinal data using machine learning techniques.

Methods

We studied patients discharged from acute care facilities in 2015 and 2016 in Alberta, Canada, excluding those who were hospitalized to give birth or for a psychiatric condition. We used population-level linked administrative hospital data from 2011 to 2017 to train prediction models using both manually derive

SUBMITTER: Davis S 

PROVIDER: S-EPMC9700920 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature