Abstract: Nocturnal hypoglycemia (NH) is a dangerous complication of insulin therapy that often goes undetected. In this study, we aimed to generate machine learning (ML)-based models for short-term NH prediction in hospitalized patients with type 1 diabetes (T1D). The models were trained on continuous glucose monitoring (CGM) data obtained from 406 adult patients admitted to a tertiary referral hospital. Eight CGM-derived metrics of glycemic control and glucose variability were included in the models. Combinations of CGM and clinical data (23 parameters) were also assessed. Random Forest (RF), Logistic Linear Regression with Lasso regularization, and Artificial Neuron Networks algorithms were applied. In our models, RF provided the best prediction accuracy with 15 min and 30 min prediction horizons. The addition of clinical parameters slightly improved the prediction accuracy of most models, whereas oversampling and undersampling procedures did not have significant effects. The areas under the curve of the best models based on CGM and clinical data with 15 min and 30 min prediction horizons were 0.97 and 0.942, respectively. Basal insulin dose, diabetes duration, proteinuria, and HbA1c were the most important clinical predictors of NH assessed by RF. In conclusion, ML is a promising approach to personalized prediction of NH in hospitalized patients with T1D.

Cite: Berikov V.B. , Kutnenko O.A. , Semenova J.F. , Klimontov V.V.
Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes
In compilation Selected papers from the 2nd conference with international participation “Basic research in endocrinology: a modern strategy for the development and technologies of personalized medicine”. – MDPI AG, Базель., 2025. – C.129-139. – ISBN 978-3-7258-4017-5. РИНЦ

Dates:

Submitted:	Jun 21, 2022
Accepted:	Jul 29, 2022
Published print:	Jun 23, 2025
Published online:	Jun 23, 2025

Identifiers:

Elibrary:

82490186

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