TY - GEN
T1 - Fuzzy logic-based covid-19 and other respiratory conditions pre-clinical diagnosis system
AU - Orozco-del-Castillo, M. G.
AU - Novelo-Cruz, R. A.
AU - Hernández-Gómez, J. J.
AU - Mena-Zapata, P. A.
AU - Brito-Borges, E.
AU - Álvarez-Pacheco, A. E.
AU - García-Gutiérrez, A. E.
AU - Yáñez-Casas, G. A.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The COVID-19 disease, caused by a new coronavirus known as SARS-CoV-2, has recently emerged and caused the death of thousands of persons all around the world. One of the main issues with the disease has been, on one hand, the saturation of the medical personnel, and on the other, the untimely search of medical attention by patients who could confuse the symptoms with other common respiratory conditions with similar symptomatology. Since AI approaches based on machine learning depend on large training datasets, currently neither easily accessible nor reliable, a COVID-19 pre-clinical diagnosis system using a fuzzy inference system is constructed, which is also capable of contrasting it with other respiratory conditions, particularly allergies, common cold and influenza. With the use of this fuzzy inference system, complex decisions in the medical field could be able to be determined more effectively.
AB - The COVID-19 disease, caused by a new coronavirus known as SARS-CoV-2, has recently emerged and caused the death of thousands of persons all around the world. One of the main issues with the disease has been, on one hand, the saturation of the medical personnel, and on the other, the untimely search of medical attention by patients who could confuse the symptoms with other common respiratory conditions with similar symptomatology. Since AI approaches based on machine learning depend on large training datasets, currently neither easily accessible nor reliable, a COVID-19 pre-clinical diagnosis system using a fuzzy inference system is constructed, which is also capable of contrasting it with other respiratory conditions, particularly allergies, common cold and influenza. With the use of this fuzzy inference system, complex decisions in the medical field could be able to be determined more effectively.
KW - Alergies
KW - Artificial intelligence
KW - Assessment
KW - COVID-19
KW - Common cold
KW - Coronavirus
KW - Diagnostics
KW - Disease
KW - Fuzzy inference system
KW - Fuzzy logic
KW - Infectious
KW - Influenza
KW - Pre-clinic
KW - Respiratory
KW - SARS-CoV-2
KW - Symptoms
UR - http://www.scopus.com/inward/record.url?scp=85096612436&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62554-2_29
DO - 10.1007/978-3-030-62554-2_29
M3 - Contribución a la conferencia
AN - SCOPUS:85096612436
SN - 9783030625535
T3 - Communications in Computer and Information Science
SP - 402
EP - 419
BT - Telematics and Computing - 9th International Congress, WITCOM 2020, Proceedings
A2 - Mata-Rivera, Miguel Félix
A2 - Zagal-Flores, Roberto
A2 - Barria-Huidobro, Cristian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Congress on Telematics and Computing, WITCOM 2020
Y2 - 2 November 2020 through 6 November 2020
ER -