Machine Learning With Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort.

ABSTRACT:

Background

Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk presents opportunities for actionable change to reduce readmission rates.

Objective

To combine machine learning classification and feature importance analysis to identify drivers of readmission in a large cohort of spine patients.

Methods

Cases involving surgical procedures for degenerative spine conditions between 2008 and 2016 were retrospectively reviewed. Of 11 150 cases, 396 patients (3.6%) experienced an unplanned hospital readmission within 30 d of discharge. Over 75 pre-discharge variables were collected and categorized into demographic, perioperative, and resource utilization feature domains. Random forest classif

SUBMITTER: Martini ML 

PROVIDER: S-EPMC12311961 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature