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Title:PODATKOVNO RUDARJENJE V LEKARNIŠKI DEJAVNOSTI
Authors:ID Pečnik, Vlado (Author)
ID Ojsteršek, Milan (Mentor) More about this mentor... New window
ID Podgorelec, Vili (Comentor)
Files:.pdf VS_Pecnik_Vlado_2009.pdf (2,81 MB)
MD5: 3692FABC0E6A6F72C665EE5DA23A44DB
PID: 20.500.12556/dkum/8f7917b0-084f-41d7-813e-9a43643ddc51
 
Language:Slovenian
Work type:Undergraduate thesis
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomski nalogi so najprej opisane osnovne metode in algoritmi podatkovnega rudarjenja, kot so linearna regresija, histogram, analiza gruc, povezovalna pravila odlocitvena drevesa, nevronske mreže, algoritem K–ti najbližji sosed in algoritem C4.5. Na podrocju oskrbe bolnikov z zdravili so se pokazale potrebe po rudarjenju po podatkovnih bazah tudi v farmacevtski dejavnosti. Z uporabo algoritma C4.5 za gradnjo odlocitvenih dreves v orodju WEKA smo iz podatkov lekarniške podatkovne baze ugotavljali zakonitosti pri predpisovanju registriranih zdravil.
Keywords:podatkovno rudarjenje, polifarmakoterapija, polipragmazija, interakcije med zdravili, WEKA.
Place of publishing:Maribor
Publisher:[V. Pečnik]
Year of publishing:2009
PID:20.500.12556/DKUM-10183 New window
UDC:004.6
COBISS.SI-ID:51131907 New window
NUK URN:URN:SI:UM:DK:KWK4OB9S
Publication date in DKUM:02.02.2012
Views:3671
Downloads:552
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:DATA MINING IN FARMACY
Abstract:The first part of diploma paper deals with data mining basic methods and algorithms, such as linear regression, histogram, cluster analysis, binding rules, decision trees, neural networks, K-nearest neighbour, and the C4.5 algorithm. In the field of treating patients with drugs, a need for data mining arose in pharmaceutical business as well. Using C4.5 algorithm for building decision trees in WEKA tool and information from the pharmacy database we were assessing basic rules that apply in prescribing registered drugs.
Keywords:data mining, polypharmacy, polypragmasy, drug interaction, WEKA.


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