Knowledge discovery of drug data on the example of adverse reaction prediction

BMC Bioinformatics

Research

Ozgur Ilyas Ekmekci¹  Pinar Yildirim¹  Andreas Holzinger²  Ljiljana Majnarić³ 

[1] Department of Computer Engineering, Faculty of Engineering & Architecture, Okan University, Istanbul, Turkey;Institute for Medical Informatics, Statistics & Documentation, Medical University of Graz, Graz, Austria;School of Medicine, University J.J. Strossmayer Osijek, Crotia;

关键词: Knowledge Discovery;    Data Mining;    adverse reactions and allergy (ARA);    k-means algorithm;   

DOI  :  10.1186/1471-2105-15-S6-S7

来源: Springer

【 摘 要 】

BackgroundAntibiotics are the widely prescribed drugs for children and most likely to be related with adverse reactions. Record on adverse reactions and allergies from antibiotics considerably affect the prescription choices. We consider this a biomedical decision-making problem and explore hidden knowledge in survey results on data extracted from a big data pool of health records of children, from the Health Center of Osijek, Eastern Croatia.ResultsWe applied and evaluated a k-means algorithm to the dataset to generate some clusters which have similar features. Our results highlight that some type of antibiotics form different clusters, which insight is most helpful for the clinician to support better decision-making.ConclusionsMedical professionals can investigate the clusters which our study revealed, thus gaining useful knowledge and insight into this data for their clinical studies.

【 授权许可】

CC BY   
© Yildirim et al.; licensee BioMed Central Ltd. 2014