TheraWorld: A Medical World Model in Hyperbolic Space for Treatment-Driven Disease Trajectory Simulation

Modeling treatment-driven disease trajectories is crucial for clinical decision support. However, existing large language models typically rely on Euclidean representation spaces that poorly capture the hierarchical structure of medical concepts and the rapidly expanding state space of severe patients. We introduce TheraWorld, a medical world model that predicts future lab values and patient-state trends from historical records and observed treatment sequences in hyperbolic space. TheraWorld adapts pretrained LLMs with LoRA in hyperbolic space, and further introduces hyperbolic consistency and severity-aware radial regularization to preserve clinical structure and align representation geometry with patient severity. We construct a treatment-round-based EHR dataset and evaluate TheraWorld across multiple model scales. TheraWorld achieves the best average score of 72.10, outperforming medical LLMs and large models such as MedGemma-27B (67.30) and GLM-5.1 (69.06). Further analysis shows that the learned hyperbolic radius reflects patient severity, demonstrating the interpretability of hyperbolic geometry for LLM-based medical world modeling.