LLMs Accelerate Annotation for Medical Information Extraction

TL;DR

This paper presents a method combining Large Language Models with human expertise to efficiently generate labeled data for medical information extraction, reducing annotation effort while maintaining high accuracy.

Contribution

It introduces a novel approach that leverages LLMs alongside human annotators to accelerate and improve medical text annotation processes.

Findings

Significantly reduces human annotation effort

Maintains high accuracy in medical information extraction

Enables rapid creation of labeled datasets for healthcare NLP

Abstract

The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challenging to access or interpret. To uncover this hidden information, specialized Natural Language Processing (NLP) models are required. However, training these models necessitates large amounts of labeled data, a process that is both time-consuming and costly when relying solely on human experts for annotation. In this paper, we propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating ground truth labels for medical text annotation. By utilizing LLMs in conjunction with human annotators, we significantly reduce the human annotation burden, enabling the rapid creation of labeled datasets. We rigorously evaluate our method on a medical information extraction task, demonstrating that our approach not only substantially cuts down on human intervention but also maintains high accuracy. The results highlight the potential of using LLMs to improve the utilization of unstructured clinical data, allowing for the swift deployment of tailored NLP solutions in healthcare.