Deep representation learning for clustering longitudinal survival data from electronic health records Article Swipe
Jiajun Qiu
,
Yao Hu
,
Li Li
,
A. Mesut Erzurumluoglu
,
Ingrid Brænne
,
Charles E. Whitehurst
,
Jochen Schmitz
,
Jatin Arora
,
Boris Bartholdy
,
Shrey Gandhi
,
Pierre Khoueiry
,
Stefanie Mueller
,
Boris Noyvert
,
Zhihao Ding
,
Jan Jensen
,
Johann de Jong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-56625-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-56625-z
Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing approaches fail to adequately capture complex interactions between diagnosis trajectories and disease-relevant risk events, leading to subgroups that can still display great heterogeneity in event risk and underlying molecular mechanisms. To address this challenge, we implemented VaDeSC-EHR, a transformer-based variational autoencoder for clustering longitudinal survival data as extracted from electronic health records. We show that VaDeSC-EHR outperforms baseline methods on both synthetic and real-world benchmark datasets with known ground-truth cluster labels. In an application to Crohn’s disease, VaDeSC-EHR successfully identifies four distinct subgroups with divergent diagnosis trajectories and risk profiles, revealing clinically and genetically relevant factors in Crohn’s disease. Our results show that VaDeSC-EHR can be a powerful tool for discovering novel patient subgroups in the development of precision medicine approaches.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-56625-z
- https://www.nature.com/articles/s41467-025-56625-z.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408460145
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- OpenAlex ID
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https://openalex.org/W4408460145Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41467-025-56625-zDigital Object Identifier
- Title
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Deep representation learning for clustering longitudinal survival data from electronic health recordsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-14Full publication date if available
- Authors
-
Jiajun Qiu, Yao Hu, Li Li, A. Mesut Erzurumluoglu, Ingrid Brænne, Charles E. Whitehurst, Jochen Schmitz, Jatin Arora, Boris Bartholdy, Shrey Gandhi, Pierre Khoueiry, Stefanie Mueller, Boris Noyvert, Zhihao Ding, Jan Jensen, Johann de JongList of authors in order
- Landing page
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https://doi.org/10.1038/s41467-025-56625-zPublisher landing page
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https://www.nature.com/articles/s41467-025-56625-z.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41467-025-56625-z.pdfDirect OA link when available
- Concepts
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Autoencoder, Computer science, Cluster analysis, Health records, Machine learning, Artificial intelligence, Benchmark (surveying), Data mining, Data science, Disease, Deep learning, Medicine, Health care, Cartography, Geography, Economics, Economic growth, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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