GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models

TL;DR

GAMedX leverages large language models to improve medical entity extraction from unstructured clinical texts, achieving high accuracy and streamlining data processing in healthcare records.

Contribution

This paper introduces GAMedX, a novel LLM-based NER approach that effectively extracts medical entities from unstructured narratives, enhancing accuracy and scalability.

Findings

Achieved 98% accuracy in entity extraction

Significant ROUGE F1 score on evaluation datasets

Provides a scalable, cost-effective data extraction solution

Abstract

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently extract entities from medical narratives and unstructured text generated throughout various phases of the patient hospital visit. By addressing the significant challenge of processing unstructured medical text, GAMedX leverages the capabilities of generative AI and LLMs for improved data extraction. Employing a unified approach, the methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98\%. This innovation enhances entity extraction, offering a scalable, cost-effective solution for automated forms filling from unstructured data. As a result, GAMedX streamlines the processing of unstructured narratives, and sets a new standard in NER applications, contributing significantly to theoretical and practical advancements beyond the medical technology sphere.