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MAS4GC: Multi-agent System for Glycemic Control of Intensive Care Unit Patients

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Intelligent Systems (BRACIS 2021)

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

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Abstract

The coronavirus disease (COVID-19) pandemic has brought significant challenges worldwide through the consequences of increasing demand for the Intensive Care Unit (ICU) resources. This work presents the Multi-Agent System for Glycemic Control (MAS4GC) to assist health professionals leading with critical patients in the ICU. More specifically, the MAS4GC manages patients’ blood glucose through glycemic predictions, treatment, and monitoring recommendations to health professionals. Prediction models are applied to monitor patients’ blood glucose allowing health professionals to carry out preventive treatments. The glycemic control is included in the FAST HUG mnemonic to remember the key issues in the supportive care of critically ill patients. The MAS4GC methodological development process is presented with Tropos modeling, architectural design, and implementation with the PADE framework. Agents’ inference mechanisms are based on production rules defined by intensive care physician specialists applying their knowledge to indicate treatments for patients. Two experiments using patients with synthetic data were conducted to evaluate the results of the MAS4GC: (1) the prediction model achieved 90% accuracy in blood glucose predictions for the next 4 h, (2) 84% similarity of treatment recommendations compared to a human specialist, and 78% in recommendations for monitoring glycemic of critical patients.

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Notes

  1. 1.

    Available at http://glycon.herokuapp.com/.

  2. 2.

    BMI is a person’s weight in kilograms divided by the square of height in meters, it is the adult body mass index.

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Acknowledgement

Prof. C. G. Ralha thanks the financial support from the Brazilian National Council for Scientific and Technological Development (CNPq) under grant number 311301/2018-5.

Author information

Authors and Affiliations

  1. Instituto Federal de Brasília (IFB), Brasília, Brazil

    Tiago Henrique Faccio Segato & Rafael Moura da Silva Serafim

  2. Universidade de Brasília (UnB), Brasília, Brazil

    Tiago Henrique Faccio Segato & Célia Ghedini Ralha

  3. Escola Superior de Ciências da Saúde (ESCS), Brasília, Brazil

    Sérgio Eduardo Soares Fernandes

Authors
  1. Tiago Henrique Faccio Segato
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  2. Rafael Moura da Silva Serafim
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  3. Sérgio Eduardo Soares Fernandes
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  4. Célia Ghedini Ralha
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Corresponding author

Correspondence to Tiago Henrique Faccio Segato .

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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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Segato, T.H.F., Serafim, R.M.d.S., Fernandes, S.E.S., Ralha, C.G. (2021). MAS4GC: Multi-agent System for Glycemic Control of Intensive Care Unit Patients. 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_5

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  • DOI: https://doi.org/10.1007/978-3-030-91702-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91701-2

  • Online ISBN: 978-3-030-91702-9

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