Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.

ABSTRACT:

Background

Hyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes.

Methods

We included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light G

SUBMITTER: Hsu CC 

PROVIDER: S-EPMC10594858 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature