Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs
Abstract 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...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11876-0 |
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| author | Chen Zihang Liu Liang Su Qianmin Cheng Gaoyi Huang Jihan Li Ying |
| author_facet | Chen Zihang Liu Liang Su Qianmin Cheng Gaoyi Huang Jihan Li Ying |
| author_sort | Chen Zihang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1208b08018cc44919d86facbae7812e1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1208b08018cc44919d86facbae7812e12025-08-20T04:03:07ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-11876-0Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphsChen Zihang0Liu Liang1Su Qianmin2Cheng Gaoyi3Huang Jihan4Li Ying5School of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceInstitute of Clinical Science, Zhongshan Hospital, Fudan UniversitySchool of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceSchool of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceCenter for Drug Clinical Research, Shanghai University of Traditional Chinese MedicineDepartment of Hepatology Longhua Hospital, Shanghai University of Traditional Chinese MedicineAbstract 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.https://doi.org/10.1038/s41598-025-11876-0Large Language ModelKnowledge GraphQuestionnaireClinical TrialInclusion and Exclusion Criteria |
| spellingShingle | Chen Zihang Liu Liang Su Qianmin Cheng Gaoyi Huang Jihan Li Ying Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs Scientific Reports Large Language Model Knowledge Graph Questionnaire Clinical Trial Inclusion and Exclusion Criteria |
| title | Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| title_full | Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| title_fullStr | Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| title_full_unstemmed | Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| title_short | Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| title_sort | enhanced pre recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs |
| topic | Large Language Model Knowledge Graph Questionnaire Clinical Trial Inclusion and Exclusion Criteria |
| url | https://doi.org/10.1038/s41598-025-11876-0 |
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