Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes
Journal:
arXiv
Published Date:
Apr 12, 2025
Abstract
The dead-in-bed syndrome describes the sudden and unexplained death of young
individuals with Type 1 Diabetes (T1D) without prior long-term complications.
One leading hypothesis attributes this phenomenon to nocturnal hypoglycemia
(NH), a dangerous drop in blood glucose during sleep. This study aims to
improve NH prediction in children with T1D by leveraging physiological data and
machine learning (ML) techniques. We analyze an in-house dataset collected from
16 children with T1D, integrating physiological metrics from wearable sensors.
We explore predictive performance through feature engineering, model selection,
architectures, and oversampling. To address data limitations, we apply transfer
learning from a publicly available adult dataset. Our results achieve an AUROC
of 0.75 +- 0.21 on the in-house dataset, further improving to 0.78 +- 0.05 with
transfer learning. This research moves beyond glucose-only predictions by
incorporating physiological parameters, showcasing the potential of ML to
enhance NH detection and improve clinical decision-making for pediatric
diabetes management.
