Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes
It addresses a critical safety issue for children with Type 1 Diabetes by potentially reducing risks like dead-in-bed syndrome, though it is incremental as it builds on existing prediction methods by adding physiological data.
This study tackled the problem of predicting nocturnal hypoglycemia in children with Type 1 Diabetes by integrating physiological data from wearable sensors with machine learning, achieving an AUROC of 0.75 on an in-house dataset and improving to 0.78 with transfer learning.
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.