用知识图谱辅助救援人员实时决策，提升急救效率
KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations
- 构建急救场景知识图谱，融合实时生命体征数据
- 实现基于AI的现场情境预识别与治疗推荐
- 适合急救人员、医疗调度员及应急系统开发者
近年来，全球范围内对救援行动的需求迅速增加。人口结构变化带来的受伤或健康问题风险，成为紧急呼叫的主要来源。在这些场景中，急救人员需尽快抵达患者，提供初步救治并挽救生命。他们必须在最短时间内实施个性化、优化的医疗干预，并基于现场采集的生命体征数据评估患者状况。然而，在时间紧迫的情况下，急救人员和医疗专家难以完全调动自身知识，亟需辅助与治疗建议。为此，本文提出一种基于知识图谱的中央知识表示系统，通过现场计算、评估和处理的知识，为急救人员提供智能治疗推荐。该系统利用人工智能实现对紧急情境的预识别，显著提升救治决策效率。
原文摘要 · Abstract (English)
Over the years, the need for rescue operations throughout the world has increased rapidly. Demographic changes and the resulting risk of injury or health disorders form the basis for emergency calls. In such scenarios, first responders are in a rush to reach the patient in need, provide first aid, and save lives. In these situations, they must be able to provide personalized and optimized healthcare in the shortest possible time and estimate the patients condition with the help of freshly recorded vital data in an emergency situation. However, in such a timedependent situation, first responders and medical experts cannot fully grasp their knowledge and need assistance and recommendation for further medical treatments. To achieve this, on the spot calculated, evaluated, and processed knowledge must be made available to improve treatments by first responders. The Knowledge Graph presented in this article as a central knowledge representation provides first responders with an innovative knowledge management that enables intelligent treatment recommendations with an artificial intelligence-based pre-recognition of the situation.
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