On the prediction of clusters for adverse reactions and allergies on antibiotics for children to improve biomedical decision making

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dc.authorscopusid6505872114
dc.authorscopusid8605567300
dc.authorscopusid57185444400
dc.authorscopusid23396282000
dc.contributor.authorYildirim,P.
dc.contributor.authorMajnarić,L.
dc.contributor.authorEkmekci,O.I.
dc.contributor.authorHolzinger,A.
dc.date.accessioned2024-05-25T12:31:17Z
dc.date.available2024-05-25T12:31:17Z
dc.date.issued2013
dc.departmentOkan Universityen_US
dc.department-tempYildirim P., Department of Computer Engineering, Faculty of Engineering and Architecture, Okan University, Istanbul, Turkey; Majnarić L., School of Medicine, University J.J. Strossmayer Osijek, 31 000, Osijek, Croatia; Ekmekci O.I., Department of Computer Engineering, Faculty of Engineering and Architecture, Okan University, Istanbul, Turkey; Holzinger A., Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, A-8036 Graz, Austriaen_US
dc.description.abstractIn this paper, we report on a study to discover hidden patterns in survey results on adverse reactions and allergy (ARA) on antibiotics for children. Antibiotics are the most commonly prescribed drugs in children and most likely to be associated with adverse reactions. Record on adverse reactions and allergy from antibiotics considerably affect the prescription choices. We consider this a biomedical decision problem and explore hidden knowledge in survey results on data extracted from the health records of children, from the Health Center of Osijek, Eastern Croatia. We apply the K-means algorithm to the data in order to generate clusters and evaluate the results. As a result, some antibiotics form their own clusters. Consequently, medical professionals can investigate these clusters, thus gaining useful knowledge and insight into this data for their clinical studies. © 2013 IFIP International Federation for Information Processing.en_US
dc.identifier.citation0
dc.identifier.doi10.1007/978-3-642-40511-2_31
dc.identifier.endpage445en_US
dc.identifier.isbn978-364240510-5
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-84885730612
dc.identifier.scopusqualityQ3
dc.identifier.startpage431en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-642-40511-2_31
dc.identifier.urihttps://hdl.handle.net/20.500.14517/2274
dc.identifier.volume8127 LNCSen_US
dc.language.isoen
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference on Availability, Reliability, and Security in Information Systems and HCI, CD-ARES 2013 -- 2 September 2013 through 6 September 2013 -- Regensburg -- 100102en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdverse reactions and allergy (ARA)en_US
dc.subjectbiomedical data miningen_US
dc.subjectk-means algorithmen_US
dc.subjectknowledge discoveryen_US
dc.titleOn the prediction of clusters for adverse reactions and allergies on antibiotics for children to improve biomedical decision makingen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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