Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA

TL;DR

This paper compares GPT-4 and fine-tuned KoELECTRA models for automated extraction of information from thyroid operation narratives, aiming to improve accuracy and efficiency over traditional regex-based methods in healthcare data processing.

Contribution

It introduces a novel comparison between GPT-4 and KoELECTRA for medical text extraction, highlighting the advantages of NLP models over traditional techniques.

Findings

KoELECTRA outperforms GPT-4 in extraction accuracy

NLP models surpass regex-based methods in processing free-text medical records

The study demonstrates improved efficiency in healthcare documentation analysis

Abstract

In the rapidly evolving field of healthcare, the integration of artificial intelligence (AI) has become a pivotal component in the automation of clinical workflows, ushering in a new era of efficiency and accuracy. This study focuses on the transformative capabilities of the fine-tuned KoELECTRA model in comparison to the GPT-4 model, aiming to facilitate automated information extraction from thyroid operation narratives. The current research landscape is dominated by traditional methods heavily reliant on regular expressions, which often face challenges in processing free-style text formats containing critical details of operation records, including frozen biopsy reports. Addressing this, the study leverages advanced natural language processing (NLP) techniques to foster a paradigm shift towards more sophisticated data processing systems. Through this comparative study, we aspire to unveil a more streamlined, precise, and efficient approach to document processing in the healthcare domain, potentially revolutionizing the way medical data is handled and analyzed.