Natural Language Processing (NLP) in Diabetes Research: Mining Electronic Health Records for Hidden Risk Patterns

Abstract

The increasing prevalence of diabetes mellitus and the massive accumulation of patient data in electronic health records (EHRs) present both a challenge and an opportunity for precision medicine. Conventional data analysis often overlooks the valuable insights hidden in unstructured clinical text such as physician notes, discharge summaries, and lab reports. This study employs Natural Language Processing (NLP) techniques to mine EHRs for latent diabetes risk patterns, focusing on early detection and comorbidity prediction. A hybrid framework combining tokenization, named entity recognition (NER), and word embeddings was integrated with supervised machine learning classifiers to identify key predictors from narrative data. The model was trained and validated using real-world EHR datasets encompassing demographic, clinical, and behavioral attributes. Feature importance analysis revealed significant linguistic markers associated with insulin resistance, hypertension, and lifestyle risk indicators that were previously underrepresented in structured data fields. The findings demonstrate that NLP-driven models outperform traditional rule-based systems in sensitivity and interpretability, offering a scalable approach to enhance diabetes screening and management. This research underscores the transformative potential of artificial intelligence in health informatics, bridging clinical text analytics with personalized disease prevention strategies.