Mining Patient Narratives to Analyze Lifestyle–Blood Glucose Relationships: An LLM-Based Text Mining Framework

Lifestyle-related diseases such as diabetes are closely influenced by daily habits, yet the complex interactions between lifestyle factors and blood glucose variation remain insufficiently quantified. This study proposes a natural language processing (NLP) framework that analyzes long-form illness blogs to identify lifestyle factors associated with elevated blood glucose levels. Diabetes-related narratives were collected from a Japanese illness blog portal (TOBYO) and processed through GPT-4o-based automated labeling, BERT-series contextual embeddings, and LightGBM classification. For Type 2 Diabetes classification, the model achieved an F1-score of 0.73 using JMedRoBERTa embeddings, outperforming baseline models (BERT = 0.70; Twitter-RoBERTa = 0.65). Key factors contributing to glucose elevation were identified through feature importance analysis, with dietary behavior, lack of exercise, poor sleep, and stress emerging as major contributors. These findings demonstrate the potential of combining large language models with structured machine learning to extract health-relevant knowledge from patient narratives. The proposed approach contributes to preventive healthcare by offering interpretable, data-driven insights into lifestyle–glycemic relationships, and provides a foundation for personalized diabetes risk monitoring and AI-based health management applications.