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<p>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.</p>

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Representation learning to advance multi-institutional studies with electronic health record data from US and France

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月3日

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  • 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,
  • Kevin Pan,
  • Yuk-Lam Ho,
  • Lauren Costa,
  • Vidul A. Panickan,
  • J. Michael Gaziano,
  • Kenneth D. Mandl,
  • Vianney Jouhet,
  • Rodolphe Thiebaut,
  • Zongqi Xia,
  • Kelly Cho,
  • Katherine Liao,
  • Tianxi Cai

摘要

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.