Doudou Zhou, Han Tong, Linshanshan Wang, Suqi Liu, Xin Xiong, Ziming Gan, Griffier Romain, Boris P. Hejblum, Yun‐Chung Liu, Chuan Hong, Clara-Lea Bonzel, Tianrun Cai, Tianrun Cai, Kevin Pan, Yuk‐Lam Ho, Lauren Costa, Vidul A. Panickan, J. Michael Gaziano, Kenneth D. Mandl, Vianney Jouhet, Rodolphe Thiébaut, Zongqi Xia, Kelly Cho, Katherine P. Liao, Tianxi Cai, Tianxi Cai
The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems. Authors present a framework that harmonizes electronic health record data across hospitals by integrating medical knowledge, large language models, and graph learning. It enables cross-institutional analysis without sharing patient-level data.