Developing machine learning models to personalize care levels among emergency room patients for hospital admission.

ABSTRACT:

Objective

To develop prediction models for intensive care unit (ICU) vs non-ICU level-of-care need within 24 hours of inpatient admission for emergency department (ED) patients using electronic health record data.

Materials and methods

Using records of 41 654 ED visits to a tertiary academic center from 2015 to 2019, we tested 4 algorithms-feed-forward neural networks, regularized regression, random forests, and gradient-boosted trees-to predict ICU vs non-ICU level-of-care within 24 hours and at the 24th hour following admission. Simple-feature models included patient demographics, Emergency Severity Index (ESI), and vital sign summary. Complex-feature models added all vital signs, lab results, and counts of diagnosis, imaging, procedures, medications, and lab orders.

Re

SUBMITTER: Nguyen M 

PROVIDER: S-EPMC8510323 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature