Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study.

ABSTRACT:

Background

Measurement-based care improves patient outcomes by using standardized scales, but its widespread adoption is hindered by the lack of accessible and structured knowledge, particularly in unstructured Chinese medical literature. Extracting scale-related knowledge entities from these texts is challenging due to limited annotated data. While large language models (LLMs) show promise in named entity recognition (NER), specialized prompting strategies are needed to accurately recognize medical scale-related entities, especially in low-resource settings.

Objective

This study aims to develop and evaluate MedScaleNER, a task-oriented prompt framework designed to optimize LLM performance in recognizing medical scale-related entities from Chinese medical literature.

Meth

SUBMITTER: Hao J 

PROVIDER: S-EPMC11962316 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature