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Context-Aware and Self-learning Dynamic Transport Scheduling in Hospitals

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The Semantic Web: ESWC 2017 Satellite Events (ESWC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10577))

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Abstract

The increase in available ICT infrastructure in hospitals offers cost reduction opportunities by optimizing various workflows, while maintaining quality of care. In this demonstrator-paper, we present a self-learning dashboard, for monitoring and learning the cause of delays of hospital transports. By identifying these causes, future delays in transport time can be reduced.

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Notes

  1. 1.

    www.iminds.be/en/projects/2015/03/10/aorta.

  2. 2.

    www.semanticdesktop.org/ontologies/2008/05/20/tmo/.

  3. 3.

    users.intec.ugent.be/pieter.bonte/ontology/accio.htm.

  4. 4.

    gforge.inria.fr/projects/amigo/.

  5. 5.

    www.cs.ox.ac.uk/isg/tools/RDFox/.

  6. 6.

    https://neo4j.com.

References

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Acknowledgment

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

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Correspondence to Pieter Bonte .

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Bonte, P., Ongenae, F., Schaballie, J., Vancroonenburg, W., Vankeirsbilck, B., De Turck, F. (2017). Context-Aware and Self-learning Dynamic Transport Scheduling in Hospitals. In: Blomqvist, E., Hose, K., Paulheim, H., Ławrynowicz, A., Ciravegna, F., Hartig, O. (eds) The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science(), vol 10577. Springer, Cham. https://doi.org/10.1007/978-3-319-70407-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-70407-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70406-7

  • Online ISBN: 978-3-319-70407-4

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