LLMs Accelerate Annotation for Medical Information Extraction
This addresses the challenge of efficiently creating labeled datasets for medical information extraction, enabling faster deployment of NLP solutions in healthcare, though it is incremental as it builds on existing LLM and human annotation methods.
The paper tackles the problem of high cost and time in annotating medical text for NLP models by combining LLMs with human expertise to generate ground truth labels, resulting in a significant reduction in human annotation burden while maintaining high accuracy.
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.