Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration.

ABSTRACT:

Importance

When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources.

Objective

To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs.

Design, setting, and participants

Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronaviru

SUBMITTER: Mu E 

PROVIDER: S-EPMC8846502 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature