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

  • Femke OngenaeEmail author
  • Pieter Bonte
  • Jeroen Schaballie
  • Bert Vankeirsbilck
  • Filip De Turck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

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.

Keywords

Context Data Smart Device Inductive Logic Programming Semantic Context Semantic Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This research was partly funded by the AORTA project, co-funded by the IWT, iMinds, Xperthis, Televic Healthcare, AZMM and ZNA.

References

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    Hastreiter, S., et al.: Benchmarking logistics services in German hospitals: a research status quo. In: ICSSSM, pp. 803–808 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Femke Ongenae
    • 1
    Email author
  • Pieter Bonte
    • 1
  • Jeroen Schaballie
    • 1
  • Bert Vankeirsbilck
    • 1
  • Filip De Turck
    • 1
  1. 1.IBCN Research Group, INTEC DepartmentGhent UniversityZwijnaardeBelgium

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