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CPRS: a clinical protocol recommendation system based on LLMs.

Jingkai Ruan¹, Qianmin Su², Zihang Chen¹

¹School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, ShangHai, China.

International Journal of Medical Informatics

|December 7, 2024

Summary

This study introduces a clinical trial protocol recommendation system using Large Language Models (LLMs) and knowledge graphs. The system enhances patient-protocol matching efficiency and accuracy, advancing clinical trial development.

Keywords:

Clinical trial Knowledge graph LLMs Recommendation system

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Area of Science:

Artificial Intelligence in Medicine
Clinical Trial Management

Background:

Clinical trial protocols are essential for guiding research objectives.
Large Language Models (LLMs) offer advanced semantic capabilities for research recommendations.

Purpose of the Study:

To develop a novel clinical trial protocol recommendation system.
To leverage GPT-4 and knowledge graphs for improved patient-protocol matching.

Main Methods:

A system combining GPT-4 and knowledge graphs was developed.
Knowledge graphs identified similar clinical trial projects.
GPT-4 provided targeted patient recommendations based on semantic analysis.

Main Results:

GPT-4 outperformed SBERT models in sorting protocols by similarity.
The system demonstrated enhanced matching efficiency and accuracy for patients.
The system contributes to the automation of clinical trial protocol recommendations.

Conclusions:

Integrating knowledge graphs and LLMs improves understanding and processing of clinical trial data.
The system enhances matching efficiency and accuracy for patient-protocol recommendations.
This approach automates recommendations, crucial for medical research and public health.