Enhancing diabetes risk stratification through natural language processing

A multimodal data integration approach

Bibliographic Data

ID22073621
AuthorsYaoyan Lu (Guilin Medical University), Tingting Li (0009-0002-0758-3109, Guilin Medical University), Liyuan Ge (0000-0002-5158-0311, Guilin Medical University), Yujun Wei (0000-0001-5587-2680, Guilin Medical University), Yujing Li (0000-0001-6643-7676, Guilin Medical University), Shangyuhui Huang (Guilin Medical University), Tao Jiang (0000-0001-5221-2951, Guilin Medical University, corresponding author), Yanrui Chen (Guilin Medical University, corresponding author), Zhen Xiang (Guilin Medical University, corresponding author)
Year2026
Volume14
Pages1793361-1793361
Publication date2026-05-28
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1793361
PMID42293683
OpenAlexW7162671803
LanguageEN
References cited30

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 ( n = 939). Generalizability across learners was tested using random forest, XGBoost, and neural networks. 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 ( OR = 1.80, p = 0.003), hypertension ( OR = 2.12, p < 0.001), elevated HbA1c ( OR = 2.38, p < 0.001), and high BMI ( OR = 1.67, p < 0.001). Validation confirmed stability: optimism-adjusted AUC was 0.91, temporal validation yielded AUC 0.89 (performance decay <3%), and all four classifiers showed consistent improvement with NLP features (ΔAUC: +0.07 to +0.09). 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

Cohort · Framingham Risk Score · Generalizability theory · Logistic regression · Random forest · Risk assessment · Unstructured data · Artificial Intelligence in Healthcare · Artificial Intelligence in Healthcare and Education · Machine Learning in Healthcare

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Open DOIOpen Access