Improving large language models for clinical named entity recognition via prompt engineering.

ABSTRACT:

Importance

The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

Objectives

This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

Materials and methods

We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11339492 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature