Generalizable clinical note section identification with large language models.

ABSTRACT:

Objectives

Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations.

Materials and methods

We framed section identification as question-answering and provided the section definitions in free-text. We evaluated multiple LLMs off-the-shelf without any training. We also fine-tune our LLMs to investigate how the size and the specificity of the fine-tuning dataset impacts model performance.

Results

GPT4 achieved the highest F1 score of 0.77. The best open-source model

SUBMITTER: Zhou W 

PROVIDER: S-EPMC11319784 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature