An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study.

ABSTRACT:

Background

The study of disease progression relies on clinical data, including text data, and extracting valuable features from text data has been a research hot spot. With the rise of large language models (LLMs), semantic-based extraction pipelines are gaining acceptance in clinical research. However, the security and feature hallucination issues of LLMs require further attention.

Objective

This study aimed to introduce a novel modular LLM pipeline, which could semantically extract features from textual patient admission records.

Methods

The pipeline was designed to process a systematic succession of concept extraction, aggregation, question generation, corpus extraction, and question-and-answer scale extraction, which was tested via 2 low-parameter LLMs: Qwen-14B-C

SUBMITTER: Wang L 

PROVIDER: S-EPMC11015372 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature