Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions.

ABSTRACT:

Background

Emergency readmission poses an additional burden on both patients and healthcare systems. Risk stratification is the first step of transitional care interventions targeted at reducing readmission. To accurately predict the short- and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring system.

Methods

In this retrospective study, all emergency admission episodes from January 1st 2009 to December 31st 2016 at a tertiary hospital in Singapore were assessed. The primary outcome was time to emergency readmission within 90 days post discharge. The Score for Emergency ReAdmission Prediction (SERAP) tool was derived via an interpretable machine le

SUBMITTER: Xie F 

PROVIDER: S-EPMC8904223 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature