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A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases

Author

Listed:
  • Alberto Comesaña-Campos

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Galicia, Spain)

  • Manuel Casal-Guisande

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Galicia, Spain)

  • Jorge Cerqueiro-Pequeño

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Galicia, Spain)

  • José-Benito Bouza-Rodríguez

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Galicia, Spain)

Abstract

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to respiratory diseases. Starting from the methodology proposed by the authors in a previous work and grounded in the definition of a set of expert systems, the methodology can generate alerts about the patient’s hypoxemic status by means of the interpretation and combination of data coming both from physical measurements and from the considerations of health professionals. A concurrent set of Mamdani-type fuzzy-logic inference systems allows the collecting and processing of information, thus determining a final alert associated with the measurement of the global hypoxemic risk. This new methodology has been tested experimentally, producing positive results so far from the viewpoint of time reduction in the detection of a blood oxygen saturation deficit condition, thus implicitly improving the consequent treatment options and reducing the potential adverse effects on the patient’s health.

Suggested Citation

  • Alberto Comesaña-Campos & Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez, 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases," IJERPH, MDPI, vol. 17(22), pages 1-31, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8644-:d:448585
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    References listed on IDEAS

    as
    1. Jorge Cerqueiro-Pequeño & Alberto Comesaña-Campos & Manuel Casal-Guisande & José-Benito Bouza-Rodríguez, 2020. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks," IJERPH, MDPI, vol. 18(1), pages 1-32, December.
    2. Sam Ghazal & Michael Sauthier & David Brossier & Wassim Bouachir & Philippe A Jouvet & Rita Noumeir, 2019. "Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: A single center pilot study," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-12, February.
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    Cited by:

    1. Jorge Cerqueiro-Pequeño & Alberto Comesaña-Campos & Manuel Casal-Guisande & José-Benito Bouza-Rodríguez, 2020. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks," IJERPH, MDPI, vol. 18(1), pages 1-32, December.
    2. Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Comesaña-Campos, 2023. "Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems," Mathematics, MDPI, vol. 11(11), pages 1-33, May.
    3. Manuel Casal-Guisande & María Torres-Durán & Mar Mosteiro-Añón & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Fernández-Villar & Alberto Comesaña-Campos, 2023. "Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile," IJERPH, MDPI, vol. 20(4), pages 1-31, February.

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