Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
📰 ArXiv cs.AI
Learn how Clin-JEPA, a multi-phase co-training framework, enables joint-embedding predictive pretraining on EHR patient trajectories for improved risk-prediction tasks
Action Steps
- Apply Clin-JEPA to EHR data to obtain a single backbone for forecasting patient trajectories
- Configure the multi-phase co-training framework to optimize joint-embedding predictive pretraining
- Test the performance of Clin-JEPA on diverse downstream risk-prediction tasks
- Compare the results of Clin-JEPA with other state-of-the-art methods for EHR analysis
- Build a Clin-JEPA model using the provided architecture and training procedure to replicate the results
Who Needs to Know This
Data scientists and researchers working with electronic health records (EHRs) can benefit from this framework to improve patient trajectory forecasting and downstream risk-prediction tasks
Key Insight
💡 Clin-JEPA enables a single backbone to simultaneously forecast patient trajectories and serve diverse downstream risk-prediction tasks
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🚀 Clin-JEPA: A new multi-phase co-training framework for joint-embedding predictive pretraining on EHR patient trajectories 📊
Key Takeaways
Learn how Clin-JEPA, a multi-phase co-training framework, enables joint-embedding predictive pretraining on EHR patient trajectories for improved risk-prediction tasks
Full Article
Title: Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
Abstract:
arXiv:2605.10840v1 Announce Type: cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent-space planning in robotics and high-quality representation learning in vision, but extending the paradigm to EHR data -- to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk-prediction tasks without per-task fine-
Abstract:
arXiv:2605.10840v1 Announce Type: cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent-space planning in robotics and high-quality representation learning in vision, but extending the paradigm to EHR data -- to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk-prediction tasks without per-task fine-
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