Personalized glucose forecasting for people with type 1 diabetes using large language models

Abstract

Background and objective: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monitoring (CGM), controlling glucose levels remains challenging. Large Language Models (LLMs) have produced impressive results in text processing, but their performance with other data modalities remains unexplored. The aim of this study is three-fold. First, to evaluate the effectiveness of LLM-based models for glucose forecasting. Second, to compare the performance of different models for predicting glucose in T1D individuals treated with MDIs and pumps. Lastly, to create a personalized approach based on patient-specific training and adaptive model selection. Methods: CGM data from the T1DEXI study were used for forecasting glucose levels. Different predictive models were evaluated using the mean absolute error (MAE) and the root mean squared error and considering the Prediction Horizons (PHs) of 60, 90, and 120 min. Results: For short-term PHs (60 and 90 min), the personalized approach achieved the best results, with an average MAE of 15.7 and 20.2 for MDIs, and a MAE of 15.2 and 17.2 for pumps. For long-term PH (120 min), TIDE obtained an MAE of 19.8 for MDIs, whereas Patch-TST obtained a MAE of 18.5. Conclusion: LLM-based models provided similar MAE values to state-of-the-art models but presented a reduced variability. The proposed personalized approach obtained the best results for short-term periods. Our work contributes to developing personalized glucose prediction models for enhancing glycemic control, reducing diabetes-related complications.