ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data
arXiv:2605.21963v1 Announce Type: new Abstract: Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-disease care, yet existing electronic health record (EHR) models are predominantly discriminative, and general-purpose large language models drift under repeated interventions. We propose the \textbf{ChronoMedicalWorld Model (CMWM)}, an action-conditioned latent world-model framework for learning patient trajectories from longitudinal care data. CMWM couples a joint-embedding state encoder with a wide
The proliferation of longitudinal electronic health records and advancements in AI, particularly world models, enable more sophisticated patient trajectory modeling.
This development allows for more accurate and proactive management of chronic diseases, potentially transforming healthcare by moving from reactive treatment to predictive, personalized interventions.
The ability to simulate patient physiology over long horizons under specific interventions changes how chronic disease care is planned and executed, making it more data-driven and individualized.
- · Chronic disease patients
- · Healthcare providers
- · Pharmaceutical companies developing new therapies
- · AI healthcare tech companies
- · Traditional EHR systems
- · Generic disease management approaches
Improved long-term health outcomes and reduced healthcare costs for chronic diseases due to proactive management.
Increased demand for robust, privacy-preserving longitudinal medical data infrastructure and AI development talent in healthcare.
Ethical and regulatory challenges around AI-driven medical prognostics and interventions become a central policy debate.
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Read at arXiv cs.LG