Leveraging Natural Language Processing for Enhanced Research and Clinical Management of Thyroid Disorders through Mining Electronic Health Records: Review

Abstract

Background: Natural Language Processing (NLP) has emerged as a vital tool in the healthcare sector, particularly for mining Electronic Health Records (EHRs) to enhance research and patient care. Despite the wealth of unstructured data in EHRs, extracting useful information for clinical decision-making remains challenging. This review focuses on the applications of NLP in understanding and managing thyroid disorders, which are prevalent yet often under-researched.

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

Results: The review highlighted various NLP algorithms designed to classify thyroid nodules and predict cancer outcomes with impressive accuracy rates ranging from 77% to 100%. Notable studies demonstrated the ability of NLP to extract key data from radiology and pathology reports, improving the understanding of patient quality of life and treatment responses. Despite these advancements, the integration of NLP applications into clinical practice remains limited, with only one study employing a prospective design.

Conclusion: NLP holds significant promise for transforming the management of thyroid disorders by facilitating the extraction and analysis of unstructured data from EHRs. However, challenges such as variability in methodologies, data representation, and the need for extensive validation hinder widespread adoption. Future research should focus on optimizing NLP techniques and addressing these barriers to enhance clinical utility.