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A Conversational Agent to Support Hospital Bed Allocation

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Book cover Intelligent Systems (BRACIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073))

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Abstract

Bed allocation in hospitals is a critical and important problem, and it has become even more important since last year because of the COVID-19 pandemic. In this paper, we present an approach based on intelligent-agent technologies to assist hospital staff in charge of bed allocation. As part of this work, we developed a web-based simulation of hospital bed allocation system integrated with a chatbot for interaction with the user. As a core component in our approach, an intelligent agent uses the feedback of a plan validator to check if there are any flaws in a user-made allocation, communicating any detected problems to the user using natural language through the chatbot. Thus, our resulting application not only validates bed allocation plans but also interacts with hospital professionals using natural language communication, including giving explainable suggestions of better alternative allocations. We evaluated our approach with professionals responsible for bed allocation in two local hospitals and a doctor who provides consultancy to another local hospital. The version of the system reported in this paper addresses all the suggestions made by the specialists who evaluated its previous version.

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Notes

  1. 1.

    The web simulator code is available at https://github.com/smart-pucrs/bed-allocation-simulator.

  2. 2.

    The agent code is available at https://github.com/smart-pucrs/jason_assistant_to_bed_allocation.

  3. 3.

    The domain file with plans and problems examples are available at https://github.com/smart-pucrs/hospital-domain-PDDL.

  4. 4.

    https://dialogflow.com/.

  5. 5.

    https://cloud.google.com/functions/.

  6. 6.

    https://www.asp.net/core/overview/aspnet-vnext.

  7. 7.

    https://github.com/smart-pucrs/Dial4JaCa.

  8. 8.

    https://en.wikibooks.org/wiki/GLPK.

  9. 9.

    http://winglpk.sourceforge.net/.

  10. 10.

    All patient data in our tests are fictitious.

  11. 11.

    The validator code is available at https://github.com/smart-pucrs/PDDL-plan-validator.

  12. 12.

    https://github.com/smart-pucrs/PDDL-plan-validator/blob/main/latexOutputValidator.pdf.

  13. 13.

    https://github.com/smart-pucrs/PDDL-plan-validator.

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Acknowledgements

This research was partially funded by CNPq and CAPES – Finance Code 001.

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Authors and Affiliations

  1. School of Technology, PUCRS, Porto Alegre, RS, Brazil

    Débora C. Engelmann, Lucca Dornelles Cezar & Rafael H. Bordini

  2. DIBRIS, University of Genoa, Genoa, Italy

    Débora C. Engelmann

  3. Department of Computing, UFSC, Araranguá, SC, Brazil

    Alison R. Panisson

Authors
  1. Débora C. Engelmann
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  2. Lucca Dornelles Cezar
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  3. Alison R. Panisson
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  4. Rafael H. Bordini
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Corresponding author

Correspondence to Débora C. Engelmann .

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Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

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Engelmann, D.C., Cezar, L.D., Panisson, A.R., Bordini, R.H. (2021). A Conversational Agent to Support Hospital Bed Allocation. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-91702-9_1

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