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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F10%3A86075331%21RIV11-MSM-27240___
rdf:type
n4:Vysledek skos:Concept
dcterms:description
This paper deals with the usage of logistic regression for prediction of morbidity of patients facing colectomy. Logistic regression is frequently used in medical data analyses. Although it was not originally created for the purposes of discrimination, it can be successfully used for it as we will demonstrate. Our data comes from the patients who underwent colectomy in The Faculty Hospital Ostrava. As predictor variables for discriminant analysis was chosen the physiological and the operative scores. The physiological score comprises 12 factors, such as age, pulse, blood pressure, urea and ECG, and the operative score comprises 6 factors (seriousness of operation, loss of blood etc.). Each of these factors was graded on a four-point scale (1, 2, 4, 8). The categorical dependent variable, which we wanted to predict, was morbidity. This paper deals with the usage of logistic regression for prediction of morbidity of patients facing colectomy. Logistic regression is frequently used in medical data analyses. Although it was not originally created for the purposes of discrimination, it can be successfully used for it as we will demonstrate. Our data comes from the patients who underwent colectomy in The Faculty Hospital Ostrava. As predictor variables for discriminant analysis was chosen the physiological and the operative scores. The physiological score comprises 12 factors, such as age, pulse, blood pressure, urea and ECG, and the operative score comprises 6 factors (seriousness of operation, loss of blood etc.). Each of these factors was graded on a four-point scale (1, 2, 4, 8). The categorical dependent variable, which we wanted to predict, was morbidity.
dcterms:title
Modified logistic regression as a tool for discrimination Modified logistic regression as a tool for discrimination
skos:prefLabel
Modified logistic regression as a tool for discrimination Modified logistic regression as a tool for discrimination
skos:notation
RIV/61989100:27240/10:86075331!RIV11-MSM-27240___
n3:aktivita
n13:P
n3:aktivity
P(1M06047)
n3:dodaniDat
n6:2011
n3:domaciTvurceVysledku
n19:7761554 n19:6396038
n3:druhVysledku
n17:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
272248
n3:idVysledku
RIV/61989100:27240/10:86075331
n3:jazykVysledku
n15:eng
n3:klicovaSlova
logistic regression, discrimination analysis, medical data
n3:klicoveSlovo
n7:discrimination%20analysis n7:logistic%20regression n7:medical%20data
n3:kontrolniKodProRIV
[E260880A0AB7]
n3:mistoKonaniAkce
Prague
n3:mistoVydani
Leiden
n3:nazevZdroje
Reliability, Risk, and Safety - Theory and Applications
n3:obor
n14:BB
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n9:1M06047
n3:rokUplatneniVysledku
n6:2010
n3:tvurceVysledku
Briš, Radim Rabasová, Marcela Kuráňová, Pavlína
n3:typAkce
n20:WRD
n3:wos
000281188500271
n3:zahajeniAkce
2009-09-07+02:00
s:numberOfPages
6
n18:hasPublisher
CRC Press
n21:isbn
978-0-415-55509-8
n12:organizacniJednotka
27240