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Descripción
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| Due to the great variability of asthma symptomatology; the medical teams find practical difficulties in determining the severity of asthma. Asthma is very commonly encounter in daily medical practice. This work objective is to design a system that helps medical teams in determining the severity of asthma. Its use could reduce time, effort and cost of categorizing asthma patient. Asthma severity diagnosis is currently done by an expert person, a doctor. The motivation is to release some burden from medical team by providing them a tool that determines the severity of asthma. One of the partial goals of the work is to model the asthma problem as a fuzzy problem, because many of the symptoms can be interpreted in a fuzzy way for the diagnosis. We model the problem using the RFuzzy framework, a Prolog-based tool for representing and reasoning with fuzzy information. The fact that several researches are being done to determine the level of asthma severity developed motivates us to use a fuzzy tool to try to automatize it. Our approach is not interesting because of our medical knowledge that we have taken from some medical collaborators. The value of our work is that we have found the way of representing in a simple way the knowledge of any asthma expert for classifying automatically the severity of an asthma patient just by collecting some simple numerical data relative to the patient symptoms. Any medical professional with a different criteria for asthma classification can easily modify our system according to his/her knowledge and obtain the corresponding results. This system was developed by the participation of experienced asthma physicians and followed the global initiative for asthma (GINA) guideline. | |
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Internacional
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Si |
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Nombre congreso
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Third International Conference on Fuzzy Systems and Data Mining III |
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Tipo de participación
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960 |
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Lugar del congreso
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Hualien, Taiwan |
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Revisores
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Si |
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ISBN o ISSN
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978-1-61499-827-3 |
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DOI
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10.3233/978-1-61499-828-0-197 |
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Fecha inicio congreso
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24/11/2017 |
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Fecha fin congreso
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27/11/2017 |
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Desde la página
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197 |
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Hasta la página
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202 |
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Título de las actas
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Fuzzy Systems and Data Mining III (vol. 299 in Frontiers in Artificial Intellligence and Applications series) |