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

Alberto Comesaña-Campos, Manuel Casal-Guisande, Jorge Cerqueiro-Pequeño and José-Benito Bouza-Rodríguez
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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

IJERPH, 2020, vol. 17, issue 22, 1-31

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.

Keywords: respiratory diseases; coronavirus disease 2019 (COVID-19); hypoxemia; medical algorithm; expert systems; decision support systems; design science research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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