2025/03/25 by Beyza Cinar, Cinar, Beyza, Jennifer Daniel Onwuchekwa +3 · 2 citations
Health Professions · Medicine · #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Diabetes Management and Research #FOS: Biological sciences #FOS: Computer and information sciences #Hyperglycemia and glycemic control in critically ill and hospitalized patients #Machine Learning (cs.LG) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2504.00009
openalex publication_date 2025/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Type 1 diabetes (T1D) management can be significantly enhanced through the use of predictive machine learning (ML) algorithms, which can mitigate the risk of adverse events like hypoglycemia. Hypoglycemia, characterized by blood glucose levels below 70 mg/dL, is a life-threatening condition typically caused by excessive insulin administration, missed meals, or physical activity. Its asymptomatic nature impedes timely intervention, making ML models crucial for early detection. This study integrates short- (up to 2h) and long-term (up to 24h) prediction horizons (PHs) within a single classification model to enhance decision support. The predicted times are 5-15 min, 15-30 min, 30 min-1h, 1-2h, 2-4h, 4-8h, 8-12h, and 12-24h before hypoglycemia. In addition, a simplified model classifying up to 4h before hypoglycemia is compared. We trained ResNet and LSTM models on glucose levels, insulin doses, and acceleration data. The results demonstrate the superiority of the LSTM models when classifying nine classes. In particular, subject-specific models yielded better performance but achieved high recall only for classes 0, 1, and 2 with 98%, 72%, and 50%, respectively. A population-based six-class model improved the results with at least 60% of events detected. In contrast, longer PHs remain challenging with the current approach and may be considered with different models.