Semantic Context Consolidation and Rule Learning for Optimized Transport Assignments in Hospitals
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.
KeywordsContext Data Smart Device Inductive Logic Programming Semantic Context Semantic Rule
This research was partly funded by the AORTA project, co-funded by the IWT, iMinds, Xperthis, Televic Healthcare, AZMM and ZNA.
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