Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation.

ABSTRACT:

Background

Acute blood glucose (BG) decompensations (hypoglycemia and hyperglycemia) represent a frequent and significant risk for inpatients and adversely affect patient outcomes and safety. The increasing need for BG management in inpatients poses a high demand on clinical staff and health care systems in addition.

Objective

This study aimed to generate a broadly applicable multiclass classification model for predicting BG decompensation events from patients' electronic health records to indicate where adjustments in patient monitoring and therapeutic interventions are required. This should allow for taking proactive measures before BG levels are derailed.

Methods

A retrospective cohort study was conducted on patients who were hospitalized at a tertiary hospital in

SUBMITTER: Witte H 

PROVIDER: S-EPMC9345028 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature