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Statements

Subject Item
n2:RIV%2F00216224%3A14110%2F13%3A00065996%21RIV14-MSM-14110___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples. An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.
dcterms:title
Artificial neural networks in medical diagnosis Artificial neural networks in medical diagnosis
skos:prefLabel
Artificial neural networks in medical diagnosis Artificial neural networks in medical diagnosis
skos:notation
RIV/00216224:14110/13:00065996!RIV14-MSM-14110___
n11:predkladatel
n21:orjk%3A14110
n3:aktivita
n14:Z n14:P
n3:aktivity
P(EE2.3.20.0185), P(GA202/07/1669), Z(MSM0021622411), Z(MSM0021622430)
n3:cisloPeriodika
2
n3:dodaniDat
n6:2014
n3:domaciTvurceVysledku
n10:8064296 López Rodríguez, Alberto Amato, Filippo n10:6955231 n10:6723381
n3:druhVysledku
n5:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
62141
n3:idVysledku
RIV/00216224:14110/13:00065996
n3:jazykVysledku
n16:eng
n3:klicovaSlova
medical diagnosis; artificial intelligence; artificial neural networks; cancer; cardiovascular diseases; diabetes
n3:klicoveSlovo
n4:artificial%20intelligence n4:diabetes n4:cancer n4:medical%20diagnosis n4:artificial%20neural%20networks n4:cardiovascular%20diseases
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[48D08FEAF133]
n3:nazevZdroje
Journal of Applied Biomedicine
n3:obor
n7:CB
n3:pocetDomacichTvurcuVysledku
5
n3:pocetTvurcuVysledku
6
n3:projekt
n8:EE2.3.20.0185 n8:GA202%2F07%2F1669
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
11
n3:tvurceVysledku
Vaňhara, Petr Amato, Filippo Hampl, Aleš López Rodríguez, Alberto Havel, Josef Peña-Méndez, Eladia María
n3:wos
000314809600001
n3:zamer
n19:MSM0021622411 n19:MSM0021622430
s:issn
1214-021X
s:numberOfPages
12
n18:doi
10.2478/v10136-012-0031-x
n17:organizacniJednotka
14110