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

Abstract

Diabetes continues to rise globally, driven by complex interactions of genetics, lifestyle, and healthcare disparities. Traditional statistical approaches often fail to capture subtle clinical clues buried in “Electronic Health Records (EHRs)”, including physician notes, diagnostic histories, medication patterns, and temporal disease trajectories. This study proposes an integrated “Natural Language Processing (NLP)” framework for identifying hidden risk patterns associated with early diabetes progression and related complications. The system processes unstructured clinical text from EHRs using transformer-based embeddings, temporal sequence modelling, and clinical concept extraction to reveal latent predictors of glycemic deterioration. By combining contextual word representations with risk-stratification models, the framework uncovers high-impact patterns such as symptom clustering, comorbidity interactions, medication response inconsistencies, and consultation frequencies. Experiments conducted on anonymised hospital datasets show substantial improvements in early-risk prediction accuracy, interpretability, and longitudinal monitoring compared to traditional machine-learning baselines. Moreover, the proposed model demonstrates efficiency in extracting clinically relevant biomarkers without manual feature engineering. The study highlights the potential of NLP-driven analytics for supporting personalised interventions, enhancing clinical decision support systems, and accelerating precision-diabetes research. The results provide evidence that leveraging unstructured EHR text through advanced NLP architectures is a transformative pathway for early detection, complication forecasting, and population-level diabetes surveillance.