[3H1-OS-10a] OS-10

[3H1-OS-10a-04] Knowledge Graphs and Generative AI Utilization for Exploring Disease Assessment Endpoints in Clinical Trials

〇Ryouichi Chatani¹, Shotaro Sonoike¹, Mariko Nio¹, Masayuki Kaneko¹, Isao Sano², Hiroki Kato³, Hiroko Otaki⁴, Yoichi Nakamoto³, Kunihiko Kido⁴, Naoki Fukazawa¹ (1. Clinical Development Division, Chugai Pharmaceutical Co., Ltd., 2. Digital Transformation Unit, Chugai Pharmaceutical Co., Ltd., 3. Industrial Digital Business Unit, Hitachi, Ltd., 4. Research & Development Group, Hitachi, Ltd.)

Keywords:Knowledge Graph, Generative AI, Endpoint, Clinical Trial , Link Prediction

In clinical drug development, complex information must be organized for decision-making. This process requires identifying key issues and developing effective strategies quickly. Defining endpoints (EPs) in clinical trial planning is crucial for assessing drug profiles. Knowledge graphs (KGs) integrate information based on relationships, enabling new inferences. KGs can improve comprehensiveness and efficiency in clinical development. Using generative AI as an interface cloud facilitate discussions among clinical researchers. This study explored EP search methods for clinical trials using KGs and generative AI. We integrated KEGG MEDICUS and ClinicalTrials.gov, implementing 1) 1-hop KG search, 2) 2-hop+ search, and 3) link prediction via KG completion algorithms. Results were summarized and reported using generative AI. A qualitative evaluation by clinical researchers confirmed that the proposed method provides comprehensive and efficient information extraction. Future challenges include expanding data sources and improving inference accuracy.