Enhancing Hypoglycemia Prediction in Type 1 Diabetes Through Semantic Knowledge Integration and Machine Learning Optimization

Managing type 1 diabetes (T1D) is challenging due to unpredictable hypoglycemia episodes that can lead to serious health risks. To address this issue, this research aims to develop and evaluate a novel hybrid artificial intelligence (AI) model that integrates deep semantic knowledge and optimizes machine learning (ML) model frameworks to improve the prediction of hypoglycemia in patients with T1D. The proposed model incorporates comprehensive knowledge graphs (KGs) derived from patient-specific contextual data and evidence-based information, leveraging ML approaches to provide accurate and personalized predictions. The research methodology involves data collection, ontology development, KG construction, and utilization of knowledge graph embeddings (KGEs) to convert entities within the constructed graph into numerical vectors to enhance ML predictions. Preliminary findings indicate that integrating semantic knowledge with ML techniques can reveal complex patterns and patient-specific factors and hence, demonstrate promising performance in improving the predictive accuracy of hypoglycemia and enabling more personalized, effective management strategies. Future work will focus on developing the hybrid AI model to improve early detection and proactive management of hypoglycemia, implementing real-world validation for personalized hypoglycemia prediction, as well as evaluating its scalability and generalizability across diverse populations and clinical settings.