Comparison of logistic regression and artificial neural network models for predicting hypoglycemia in non-ICU inpatients with diabetes.

ABSTRACT:

Aim

To analyze data from non-intensive care unit (non-ICU) inpatients with diabetes to predict the risk of hypoglycemia using electronic health records (EHRs) and point-of-care (POC) blood glucose values.

Methods

Patient demographics, laboratory results, POC blood glucose, and procedures were performed during the hospital stays on Days 0-2 to predict hypoglycemic episodes (blood glucose ≤ 3.9 mmol/L) on Days 3-6. The dataset was randomly split into a training set and an independent verification set at a 7:3 ratio. Logistic Regression (LR) and Artificial Neural Network (ANN) were compared using the area under the curve (AUC). A nomogram plot was also constructed to display the predicted hypoglycemia probabilities.

Results

Data from 16,593 diabetic patients (January 201

SUBMITTER: Shao F 

PROVIDER: S-EPMC12802217 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature