Span-based annotation framework for LLM-based clinical named entity recognition: development and validation using Korean emergency department notes.

ABSTRACT:

Objective

This study aims to develop and validate of a span-based annotation framework for clinical named entity recognition (NER) using large language models (LLMs) based on Korean emergency department clinical notes.

Materials and methods

Two datasets with the same entity types but different annotation spans (word- vs phrase-level) were constructed, with the phrase-level dataset further was expanded into a doubled version. A Korean language-specific LLM was fine-tuned on each dataset, producing three variants that were compared with two baseline models, few-shot LLM and fine-tuned small language model (SLM). The final variant fine-tuned on the doubled phrase-level dataset was further evaluated against a human annotator.

Results

In all experimental settings, three va

SUBMITTER: Jang EH 

PROVIDER: S-EPMC12657458 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature