SepsisAgent

Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model

⚡ Introduction

**SepsisAgent** is a world model-augmented LLM agent for ICU sepsis treatment recommendation. It combines an LLM policy with a learned **Clinical World Model** that simulates patient responses under candidate fluid-vasopressor interventions. Instead of directly outputting a treatment action, SepsisAgent follows a **propose-simulate-refine** workflow: it proposes candidate actions, queries the world model for counterfactual patient trajectories, and refines the final prescription using both simulated dynamics and clinical priors.

The agent is trained with a three-stage curriculum: patient-dynamics supervised fine-tuning, propose-simulate-refine behavior cloning, and world-model-based agentic reinforcement learning. On MIMIC-IV sepsis trajectories, SepsisAgent improves off-policy treatment value while maintaining strong guideline adherence and low unsafe-action rates.

🧠 Method Overview

SepsisAgent uses a Clinical World Model as both an inference-time simulator and a training environment. The world model predicts action-conditioned patient evolution, while the LLM agent learns how to interpret these simulated responses for long-horizon treatment planning.

📊 Main Results

Clinical World Model Evaluation

Model Component	Metric	Value
State Transition	MAE	0.316
State Transition	Vent