Utilizing Natural Language Processing to Improve Research and Clinical Management of Thyroid Disorders through Electronic Health Record Analysis: A Comprehensive Review

Abstract

Background: Natural Language Processing (NLP) has become an essential tool in healthcare, particularly for analyzing Electronic Health Records (EHRs) to improve research and patient care. Despite the abundance of unstructured data in EHRs, extracting relevant information for clinical decision-making remains a challenge. This review examines the application of NLP in the understanding and management of thyroid disorders, which are common yet often inadequately researched.

Methods: A systematic review was carried out, sourcing studies published from 2012 to 2023 across databases such as MEDLINE, EMBASE, and Scopus. The search strategy, developed by an experienced librarian, focused on studies utilizing NLP to analyze EHR data related to thyroid disorders, including thyroid nodules and cancer.

Results: The review identified various NLP algorithms used to classify thyroid nodules and predict cancer outcomes, achieving accuracy rates ranging from 77% to 100%. Noteworthy studies demonstrated the capacity of NLP to extract valuable data from radiology and pathology reports, enhancing the understanding of patient quality of life and treatment responses. Despite these advancements, the integration of NLP into clinical practice remains limited, with only one study utilizing a prospective design.

Conclusion: NLP shows considerable potential for revolutionizing the management of thyroid disorders by enabling the extraction and analysis of unstructured EHR data. However, challenges such as methodological variability, data representation issues, and the need for extensive validation impede broader adoption. Future research should aim to refine NLP techniques and address these challenges to improve clinical application.