Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models.

ABSTRACT:

Objectives

The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR.

Materials and methods

We annotated the criteria, risk factors, and preventive medical services described in the adult guidelines published by United States Preventive Services Taskforce and evaluated 3 state-of-the-art LLMs on extracting information in these categories from the guidelines automatically.

Results

We included 24 gui

SUBMITTER: Luo X 

PROVIDER: S-EPMC11258407 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature