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Semantic Context Consolidation and Rule Learning for Optimized Transport Assignments in Hospitals

机译:语义上下文合并和规则学习,以优化医院中的运输分配

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The increase of ICT infrastructure in hospitals offer opportunities for cost reduction by optimizing workflows, while maintaining quality of care. This work-in-progress poster details the AORTA system, which is a semantic platform to optimize transportation task scheduling and execution in hospitals. It provides a dynamic scheduler with an up-to-date view about the current context by performing semantic reasoning on the information provided by the available software tools and smart devices. Additionally, it learns semantic rules based on historical data in order to avoid future delays in transportation time.
机译:医院信息通信技术基础设施的增加为优化工作流程同时保持护理质量提供了降低成本的机会。这张正在进行中的海报详细介绍了AORTA系统,该系统是一个语义平台,用于优化医院中的运输任务计划和执行。通过对可用软件工具和智能设备提供的信息执行语义推理,它为动态调度程序提供了有关当前上下文的最新视图。此外,它还基于历史数据学习语义规则,以避免将来运输时间的延迟。

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