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Intelligent Medicine
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2026年4期
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Optimizing dynamic diversion decision-making in mass gatherings through multi-agent reinforcement learning: A retrospective real-world data-driven simulation study
Optimizing dynamic diversion decision-making in mass gatherings through multi-agent reinforcement learning: A retrospective real-world data-driven simulation study
摘要
Background:Mass gatherings pose significant risks of mass casualty incidents, where static triage protocols often fail to balance individual patient urgency with global emergency resource constraints, leading to preventable mortality. We aimed to evaluate a dynamic diversion strategy optimized using machine learning to improve survival outcomes and emergency efficiency in such scenarios.Methods:We conducted a retrospective simulation study based on real-world emergency data from two major events in Hangzhou, China: The 2024 Hangzhou Marathon and Linping Ice and Snow World fire incident. Data encompassing patient severity such as respiratory rate, pulse rate, and motor response (RPM) scores, ambulance logistics, and real-time hospital bed capacity from multiple trauma centers were used to construct the simulation environment. We compared a dynamic diversion framework (using a value decomposition network-based multiagent approach) against standard clinical protocols (nearest-hospital and designated-hospital strategies). Primary outcomes included overall survival rate, average waiting time for critical care, and total length of hospital stays.Results:In simulated mass casualty incident scenarios with rapid casualty influx and limited resources, our dynamic strategy significantly outperformed conventional methods. Compared to two traditional baselines, our approach boosts average patient survival rates by 6.8% and 8.3% during casualty surges, and by 13.8% and 8.8% during gradual influxes. Under resource-sufficient conditions, the respective gains are 12.2% and 8.7%. These results indicate that our method can effectively integrate and use dynamically changing emergency medical resources to achieve efficient triage and transportation decisions when dealing with mass casualties.Conclusion:Our diversion decision-making method can effectively use the large amount of dynamic emergency medical information generated during mass gatherings. It enables diversion decisions based on patient injuries and emergency medical resources, thereby providing emergency diversion doctors with more personalized transfer decision-making schemes.
年，卷(期)：
2026, 06(4)
在线出版日期：
2026年8月28日
