{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-HMB5TDVT/8106eea3-496b-48d7-81b5-eec96a1bb93b/PDF","dcterms:extent":"668 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:doc-HMB5TDVT/910c9e54-d27a-47ca-9e5e-9aed8dcc5390/TEXT","dcterms:extent":"0 KB"}],"edm:TimeSpan":{"@rdf:about":"1992-2025","edm:begin":{"@xml:lang":"en","#text":"1992"},"edm:end":{"@xml:lang":"en","#text":"2025"}},"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:doc-HMB5TDVT","dcterms:isPartOf":[{"@rdf:resource":"https://www.dlib.si/details/URN:NBN:SI:spr-FNIFVE9S"},{"@xml:lang":"sl","#text":"Radiology and oncology (Ljubljana)"}],"dcterms:issued":"2022","dc:creator":["Križmarić, Miljenko","Mlinarič, Marko","Repše-Fokter, Alenka","Takač, Iztok"],"dc:format":[{"@xml:lang":"sl","#text":"letnik:56"},{"@xml:lang":"sl","#text":"številka:iss. 3"},{"@xml:lang":"sl","#text":"str. 355-364"}],"dc:identifier":["DOI:10.2478/raon-2022-0023","COBISSID:115112451","ISSN:1318-2099","URN:URN:NBN:SI:doc-HMB5TDVT"],"dc:language":"en","dc:publisher":[{"@xml:lang":"sl","#text":"Croatian Medical Association - Croatian Society of Radiology"},{"@xml:lang":"sl","#text":"Slovenian Medical Society - Section of Radiology"}],"dc:subject":[{"@xml:lang":"en","#text":"artificial neural networks"},{"@xml:lang":"en","#text":"conisation"},{"@xml:lang":"en","#text":"displazija materničnega vratu"},{"@xml:lang":"en","#text":"konizacija"},{"@xml:lang":"en","#text":"rak materničnega vratu"},{"@xml:lang":"en","#text":"umetne nevronske mreže"},{"@xml:lang":"en","#text":"uterine cervical cancer"},{"@xml:lang":"en","#text":"uterine cervical dysplasia"}],"dcterms:temporal":{"@rdf:resource":"1992-2025"},"dc:title":{"@xml:lang":"sl","#text":"Identification of women with high grade histopathology results after conisation by artificial neural networks|"},"dc:description":{"@xml:lang":"sl","#text":"Background: The aim of the study was to evaluate if artificial neural networks can predict high-grade histopathology results after conisation from risk factors and their combinations in patients undergoing conisation because of pathological changes on uterine cervix. Patients and methods: We analysed 1475 patients who had conisation surgery at the University Clinic for Gynaecology and Obstetrics of University Clinical Centre Maribor from 1993-2005. The database in different datasets was arranged to deal with unbalance data and enhance classification performance. Weka open-source software was used for analysis with artificial neural networks. Last Papanicolaou smear (PAP) and risk factors for development of cervical dysplasia and carcinoma were used as input and high-grade dysplasia Yes/No as output result. 10-fold cross validation was used for defining training and holdout set for analysis. Results: Baseline classification and multiple runs of artificial neural network on various risk factors settings were performed. We achieved 84.19% correct classifications, area under the curve 0.87, kappa 0.64, F-measure 0.884 and Matthews correlation coefficient (MCC) 0.640 in model, where baseline prediction was 69.79%. Conclusions: With artificial neural networks we were able to identify more patients who developed high-grade squamous intraepithelial lesion on final histopathology result of conisation as with baseline prediction. But, characteristics of 1475 patients who had conisation in years 1993-2005 at the University Clinical Centre Maribor did not allow reliable prediction with artificial neural networks for every-day clinical practice"},"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:doc-HMB5TDVT","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:doc-HMB5TDVT"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:doc-HMB5TDVT/8106eea3-496b-48d7-81b5-eec96a1bb93b/PDF"},"edm:rights":{"@rdf:resource":"http://creativecommons.org/licenses/by/4.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Društvo radiologije in onkologije"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:doc-HMB5TDVT/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:doc-HMB5TDVT"}}}}