CPRS: a clinical protocol recommendation system based on LLMs.

Background: As fundamental documents in clinical trials, clinical trial protocols are intended to ensure that trials are conducted according to the objectives set by researchers. The advent of large models with superior semantic performance compared to traditional models provides fresh perspectives for research recommendations in clinical trial protocols.

Method: A clinical trial protocol recommendation system based on Large Language Models (LLMs) is proposed in this paper, combining GPT-4 and knowledge graph to assist in clinical trial protocol recommendations. Using knowledge graphs as an auxiliary tool, a finite set of clinical trial projects with similar features is identified. Subsequently, through the semantic capabilities of GPT-4, targeted recommendations are made to patients.

Results: Experiments were conducted to compare GPT-4 and multiple models from the SBERT family that handle semantic similarity. The results indicate that GPT-4 is capable of better sorting clinical trial protocols based on similarity criteria and offering targeted recommendations to patients. Consequently, this capability meets the matching requirements between projects and patients and enhances the automation of clinical trial protocol recommendations. Additionally, in the future, personal factors of patients will be fully considered during the recommendation process to provide more accurate and personalized protocol recommendations.

Conclusion: By integrating knowledge graphs and LLMs, a better understanding and processing of clinical trial protocol information can be achieved, enabling the recommendation of appropriate protocols for patients and enhancing both matching efficiency and accuracy. Furthermore, the application of this system contributes to the automation of clinical trial protocol recommendations, playing a crucial role in medical research institutions such as clinical trial research institutes and public health management departments. Additionally, it significantly aids in advancing the development of clinical trials and the medical field at large.