🤖 AI Summary
Multi-organ system diseases—such as sepsis—exhibit complex, inter-system pathological interactions, necessitating coordinated diagnostic and therapeutic strategies; however, existing AI decision-support systems are typically confined to single-organ modeling and thus fail to enable holistic, system-level interventions. To address this, we propose a Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework: it employs organ-specific agents, incorporates explicit inter-agent communication mechanisms, and adopts a dual-layer state representation that jointly encodes local physiological metrics and global organ interdependency structures—enabling, for the first time, explicit modeling of multi-organ pathophysiological coupling and dynamic collaborative decision-making. Evaluated in a clinical sepsis simulation environment, HMARL significantly improves patient survival rates. Both qualitative and quantitative analyses demonstrate consistent superiority over single-organ baseline models. This work establishes a novel, interpretable, and scalable paradigm for AI-assisted diagnosis and treatment of multi-system diseases.
📝 Abstract
Multi-organ diseases present significant challenges due to their simultaneous impact on multiple organ systems, necessitating complex and adaptive treatment strategies. Despite recent advancements in AI-powered healthcare decision support systems, existing solutions are limited to individual organ systems. They often ignore the intricate dependencies between organ system and thereby fails to provide holistic treatment recommendations that are useful in practice. We propose a novel hierarchical multi-agent reinforcement learning (HMARL) framework to address these challenges. This framework uses dedicated agents for each organ system, and model dynamic through explicit inter-agent communication channels, enabling coordinated treatment strategies across organs. Furthermore, we introduce a dual-layer state representation technique to contextualize patient conditions at various hierarchical levels, enhancing the treatment accuracy and relevance. Through extensive qualitative and quantitative evaluations in managing sepsis (a complex multi-organ disease), our approach demonstrates its ability to learn effective treatment policies that significantly improve patient survival rates. This framework marks a substantial advancement in clinical decision support systems, pioneering a comprehensive approach for multi-organ treatment recommendations.