A novel model to label delirium in an intensive care unit from clinician actions.

ABSTRACT:

Background

In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance.

Methods

EHR data on 48,451 admissions from 2001 to 2012, avail

SUBMITTER: Coombes CE 

PROVIDER: S-EPMC7941123 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature