Unlocking Insights in Thyroid Care: Applying Natural Language Processing to Electronic Health Records for Improved Patient Stratification and Clinical Decision Support

Abstract

Background: The clinical management of thyroid disorders, ranging from autoimmune conditions to cancer, generates vast amounts of unstructured data within Electronic Health Records (EHRs). This data, embedded in clinical notes, radiology reports, and pathology summaries, contains rich phenotypic details that are largely inaccessible to traditional analytical methods, creating a significant information gap for research and precision care. Aim: This narrative review aims to synthesize the current landscape, methodologies, challenges, and future directions of applying Natural Language Processing (NLP) to mine EHRs for thyroidology. It evaluates how NLP can transform unstructured text into structured data to enhance patient stratification, support clinical decisions, and advance epidemiological research. Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, and ACL Anthology for studies published between 2010 and 2024, using keywords related to NLP, EHRs, and thyroid disorders. Relevant studies were selected and thematically analyzed. Results: The review identifies key NLP architectures—from rule-based systems to deep learning models—successfully applied to extract thyroid-specific concepts, automate TI-RADS scoring, predict outcomes, and identify adverse events. However, significant challenges persist, including data heterogeneity, clinical nuance, and ethical concerns regarding bias and generalizability. Conclusion: NLP is a powerful, transformative tool for thyroid care, poised to unlock latent insights from EHRs. Realizing its full potential requires interdisciplinary collaboration, robust validation, and the development of standardized, ethically aware frameworks to integrate these technologies into clinical workflows and research infrastructures.

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