Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data Article Swipe
Doudou Zhou
,
Hanghang Tong
,
Linshanshan Wang
,
Suqi Liu
,
Xin Xiong
,
Gan Zi-ming
,
Romain Griffier
,
Boris P. Hejblum
,
Yizhi Liu
,
Chuan Hong
,
Clara-Lea Bonzel
,
Tianrun Cai
,
Kevin Pan
,
Yuk‐Lam Ho
,
Lauren Costa
,
Vidul Ayakulangara Panickan
,
J. Michael Gaziano
,
Kenneth D. Mandl
,
Vianney Jouhet
,
Rodolphe Thiebaut
,
Zongqi Xia
,
Kelly Cho
,
Katherine P. Liao
,
Tianxi Cai
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of including multi-institutional patient-level data required to study similarities and differences across patient subgroups. To address these challenges, we developed the GAME algorithm. Tested and validated across 7 institutions and 2 languages, GAME integrates data in several levels: (1) at the institutional level with knowledge graphs to establish relationships between codes and existing knowledge sources, providing the medical context for standard codes and their relationship to each other; (2) between institutions, leveraging language models to determine the relationships between institution-specific codes with established standard codes; and (3) quantifying the strength of the relationships between codes using a graph attention network. Jointly trained embeddings are created using transfer and federated learning to preserve data privacy. In this study, we demonstrate the applicability of GAME in selecting relevant features as inputs for AI-driven algorithms in a range of conditions, e.g., heart failure, rheumatoid arthritis. We then highlight the application of GAME harmonized multi-institutional EHR data in a study of Alzheimer's disease outcomes and suicide risk among patients with mental health disorders, without sharing patient-level data outside individual institutions.
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- Type
- article
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.08547
- https://arxiv.org/pdf/2502.08547
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414561321
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414561321Canonical identifier for this work in OpenAlex
- Title
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Representation Learning to Advance Multi-institutional Studies with Electronic Health Record DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-12Full publication date if available
- Authors
-
Doudou Zhou, Hanghang Tong, Linshanshan Wang, Suqi Liu, Xin Xiong, Gan Zi-ming, Romain Griffier, Boris P. Hejblum, Yizhi Liu, Chuan Hong, Clara-Lea Bonzel, Tianrun Cai, Kevin Pan, Yuk‐Lam Ho, Lauren Costa, Vidul Ayakulangara Panickan, J. Michael Gaziano, Kenneth D. Mandl, Vianney Jouhet, Rodolphe Thiebaut, Zongqi Xia, Kelly Cho, Katherine P. Liao, Tianxi CaiList of authors in order
- Landing page
-
https://arxiv.org/abs/2502.08547Publisher landing page
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https://arxiv.org/pdf/2502.08547Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.08547Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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