Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
arXiv:2605.10840v3 Announce Type: replace-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-tuning -- remains an open challenge. Existing JEPA frameworks either discard the predictor a
The continuous advancements in AI and representation learning, coupled with increasing accessibility of extensive EHR datasets, make this a timely development aimed at overcoming current limitations in AI's application to healthcare.
This development could lead to more robust and versatile AI models for healthcare, significantly improving patient trajectory forecasting and diverse risk prediction tasks without extensive fine-tuning, thus accelerating clinical AI adoption.
A single AI backbone could potentially serve multiple critical clinical prediction tasks, shifting from task-specific models to more generalized, foundational models in medical AI.
- · Healthcare AI developers
- · Hospitals and clinics
- · Patients
- · Biotech and Pharma
- · Legacy EHR systems
- · Companies reliant on single-task AI models
Improved accuracy and efficiency in clinical forecasting and risk assessment using EHR data.
Reduced development costs and faster deployment of AI solutions in healthcare, potentially leading to more personalized and preventive medicine.
Enhanced AI-driven drug discovery and clinical trial design by better understanding disease progression and patient responses through advanced predictive modeling.
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Read at arXiv cs.AI