ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data

TL;DR

The paper introduces ChronoMedicalWorld Model (CMWM), a novel action-conditioned latent world model for predicting long-term patient health trajectories from longitudinal care data, applicable to chronic diseases.

Contribution

It presents a new framework combining a joint-embedding state encoder with a wide action encoder and a recurrent latent transition module, optimized with a multi-term objective for accurate long-horizon clinical simulation.

Findings

Achieved a 50% history rollout MAE of 7.384 in CKD eGFR forecasting.

Outperformed a tuned GPT-5.5 baseline by approximately 7% in MAE and RMSE.

Demonstrated the framework's applicability to any chronic condition with periodic clinical states.

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 action encoder that admits both structured intervention indicators and free-text communication embeddings, and trains a recurrent latent transition module under a six-term objective: next-observation supervision, next-latent prediction, SIGReg latent regularisation, and three physiology-aware shape priors (slope, continuity, large-jump penalty). A closed-loop rollout-prefix protocol matches training to deployment, so the model is optimised against the same multi-step error it exhibits at inference. As a concrete case study, we instantiate CMWM for annual estimated glomerular filtration rate (eGFR) trajectory forecasting in chronic kidney disease (CKD). On a 2{,}232-patient nephrology cohort, the CKD instantiation achieves a dynamic-50\% history rollout test mean absolute error (MAE) of 7.384 and root-mean-square error (RMSE) of 10.256, against 7.964 and 11.069 for a tuned GPT-5.5 structured-prompting baseline ($-7.28\%$ MAE, $-7.35\%$ RMSE), with the gain dominated by the dialogue portion of patient--health-coach communication. The framework is not CKD-specific: its architecture, loss design, and training protocol apply to any chronic condition that can be cast as periodic clinical state interleaved with structured and conversational interventions.