ArticleNature communications2025
Deep representation learning for clustering longitudinal survival data from electronic health records.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Clinical large language model centered on electronic medical records.NPJ digital medicine · 2026Article
- Article
- Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.NPJ digital medicine · 2026Article
- Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation.Journal of biological engineering · 2026Review
- Time and person sensitive foundation model for disease prediction and risk stratification.NPJ digital medicine · 2026Article
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- Variational Deep Alliance: A Generative Auto-Encoding Approach to Longitudinal Data Analysis.Entropy (Basel, Switzerland) · 2026Article
- Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
- Representation learning with parameterised quantum circuits for advancing speech emotion recognition.Scientific reports · 2025Article
- Beyond Fixed Thresholds: Cluster-Derived MRI Boundaries Improve Assessment of Crohn's Disease Activity.Journal of clinical medicine · 2025Article
- Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records.BioData mining · 2025Article
- Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation.Frontiers in medicine · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.