Development and Validation of an Electronic Health Record-Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment.

ABSTRACT:

Importance

Current methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy.

Objective

To develop and validate a machine learning model that predicts incident delirium risk based on electronic health data available on admission.

Design, setting, and participants

Retrospective cohort study evaluating 5 machine learning algorithms to predict delirium using 796 clinical variables identified by an expert panel as relevant to delirium prediction and consistently available in electronic health records within 24 hours of admission. The training set comprised 14 227 adult patients with non-intensive care unit hospital stays and no delirium on admission who were discharged between January 1, 20

SUBMITTER: Wong A 

PROVIDER: S-EPMC6324291 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature