T1D-MLLM: Multimodal Large Language Model and Cross-Scenario Dataset for Multi-Scenario Management of Type 1 Diabetes

The management of Type 1 Diabetes (T1D) involves comprehensive scenarios, including blood glucose prediction, risk assessment, and insulin dosing control. However, the heterogeneity of information requirements, task objectives, and behavioral logic poses challenges in constructing unified T1D management systems with conventional deep learning models, mainly attributed to insufficient capability of feature alignment and lack of high-quality multi-scenario T1D data. In this paper, we propose T1D-MLLM, the first multimodal large language model designed for unified multi-scenario T1D management, as well as construct LCT1D, a large-scale and cross-scenario T1D dataset. Specifically, T1D-MLLM integrates time-series physiological data with natural language descriptions to capture longterm dependencies across multiple management scenarios while also enhancing fine-grained perception of time-series. Meanwhile, to overcome data scarcity, we proposed a multimodal data generation paradigm based on expert strategies. By constructing task templates and applying a rule-driven alignment mechanism, we generated 150,000 high-quality expert samples with individualized physiological parameters, which provide rich and diverse training samples, significantly improving the T1D-MLLM's capabilities in heterogeneous feature alignment and cross-scenario inference. These experiments demonstrate the effectiveness of the T1D-MLLM in multi-scenarios of various tasks as a unified system, with an excellent performance that surpasses both opensource and proprietary models.