Enhancing diabetes risk stratification through natural language processing: a multimodal data integration approach.

Background: This study investigates whether integrating Natural Language Processing (NLP) with traditional clinical data improves type 2 diabetes risk prediction by extracting latent risk factors from unstructured medical text. Methods: We analyzed a public dataset of 1,879 individuals. Structured variables (BMI, HbA1c, blood pressure) were normalized, while unstructured textual entries (symptom descriptions, lifestyle notes) were processed using a BERT-based NLP pipeline for feature extraction (risk behaviors, family history). A hybrid model integrated these NLP-derived features with traditional variables using logistic regression. Performance was evaluated via accuracy, precision, recall, F1-score, and AUC-ROC. Robustness was assessed through bootstrap confidence intervals, sensitivity analysis, optimism adjustment, and temporal validation on a post-2020 cohort ( Results: NLP identified non-traditional risk indicators including sedentary occupation, poor dietary adherence, and chronic stress. The integrated model significantly outperformed the structured-only baseline (Accuracy: 88.2 vs. 76.5%; AUC-ROC: 0.92 vs. 0.83). Key independent predictors included NLP-identified sedentary behavior ( Conclusion: NLP effectively unlocks latent risk information from unstructured clinical text, significantly enhancing diabetes risk prediction. This framework enables more holistic patient risk assessment for personalized prevention. Future work requires external validation, privacy-preserving methods, and enhanced interpretability via explainable AI.