%0 Conference Proceedings
%T On the Prediction of Clusters for Adverse Reactions and Allergies on Antibiotics for Children to Improve Biomedical Decision Making
%+ Faculty of Engineering [Tuzla]
%+ Stanford School of Medicine [Stanford]
%+ Research Unit Human-Computer Interaction for Medicine & Health Care (HCI4MED)
%A Yildirim, Pinar
%A Majnarić, Ljiljana
%A Ekmekci, Ozgur, Ilyas
%A Holzinger, Andreas
%Z Part 1: Cross-Domain Conference and Workshop on Multidisciplinary Research and Practice for Information Systems (CD-ARES 2013)
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st Cross-Domain Conference and Workshop on Availability, Reliability, and Security in Information Systems (CD-ARES)
%C Regensburg, Germany
%Y Alfredo Cuzzocrea
%Y Christian Kittl
%Y Dimitris E. Simos
%Y Edgar Weippl
%Y Lida Xu
%I Springer
%3 Availability, Reliability, and Security in Information Systems and HCI
%V LNCS-8127
%P 431-445
%8 2013-09-02
%D 2013
%K Adverse reactions and allergy (ARA)
%K knowledge discovery
%K biomedical data mining
%K k-means algorithm
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X In 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.
%G English
%2 https://inria.hal.science/hal-01506778/document
%2 https://inria.hal.science/hal-01506778/file/978-3-642-40511-2_31_Chapter.pdf
%L hal-01506778
%U https://inria.hal.science/hal-01506778
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-CD-ARES
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-8127