Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs.

The recruitment of participants for clinical trials has traditionally been a passive and challenging process, leading to difficulties in acquiring a sufficient number of qualified participants in a timely manner. This issue has impeded advancements in medical research. However, recent years have seen the evolution of knowledge graphs and the introduction of large language models (LLMs), providing innovative approaches for the pre-screening and recruitment phases of clinical trials. These developments promise enhanced recruitment efficiency and increased participant involvement. To ensure the safety and efficacy of clinical trials, it is crucial to establish precise inclusion and exclusion criteria for participant selection. This paper introduces a method to optimize the pre-recruitment stage by utilizing these criteria in conjunction with the cutting-edge capabilities of knowledge graphs and LLMs. The enhanced strategy includes the automated generation of questionnaires, algorithmic evaluation of eligibility, supplemental query-response functions, and a broader participant screening reach. The application of this framework yielded a detailed clinical trial recruitment questionnaire that accurately encompasses all necessary criteria. Its JSON output is noteworthy for its precision and reliability, achieving an impressive 90% accuracy rate in summarizing patient responses. Additionally, the questionnaire's ancillary question-and-answer feature complies with stringent legal and ethical standards, meeting the requirements for practical deployment. This study validates the practicality and technological soundness of the presented approach. Utilizing this framework is expected to enhance the efficiency of trial recruitment and the level of patient participation.