Machine learning-based online prediction of nocturnal hypoglycemia in elderly patients with type 2 diabetes.

ABSTRACT:

Context

Nocturnal hypoglycemia (NH) is a common adverse event in elderly patients with type 2 diabetes (T2D). This study aims to develop a clinically applicable model for predicting the risk of NH in elderly patients with T2D.

Methods

This retrospective cohort study, conducted from May 2018 to June 2024, analyzed 1,128 elderly T2D patients undergoing continuous glucose monitoring, with an independent validation involving 100 outpatients. Clinical characteristics were collected, and feature engineering was performed to select a manageable set of clinically accessible features. An ensemble model was developed using multiple base models and a stacking approach. The best-performing model was deployed as an online risk calculator.

Results

Of the development set, 288 (25.5%

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12819298 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature