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A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases
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 th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699904/ https://www.ncbi.nlm.nih.gov/pubmed/33233826 http://dx.doi.org/10.3390/ijerph17228644 |
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author | Comesaña-Campos, Alberto Casal-Guisande, Manuel Cerqueiro-Pequeño, Jorge Bouza-Rodríguez, José-Benito |
author_facet | Comesaña-Campos, Alberto Casal-Guisande, Manuel Cerqueiro-Pequeño, Jorge Bouza-Rodríguez, José-Benito |
author_sort | Comesaña-Campos, Alberto |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7699904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76999042020-11-29 A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases Comesaña-Campos, Alberto Casal-Guisande, Manuel Cerqueiro-Pequeño, Jorge Bouza-Rodríguez, José-Benito Int J Environ Res Public Health Article 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. MDPI 2020-11-20 2020-11 /pmc/articles/PMC7699904/ /pubmed/33233826 http://dx.doi.org/10.3390/ijerph17228644 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Comesaña-Campos, Alberto Casal-Guisande, Manuel Cerqueiro-Pequeño, Jorge Bouza-Rodríguez, José-Benito A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title | A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title_full | A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title_fullStr | A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title_full_unstemmed | A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title_short | A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases |
title_sort | methodology based on expert systems for the early detection and prevention of hypoxemic clinical cases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699904/ https://www.ncbi.nlm.nih.gov/pubmed/33233826 http://dx.doi.org/10.3390/ijerph17228644 |
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