Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

dc.authorscopusid 6505872114
dc.authorwosid YILDIRIM, PINAR/X-1182-2019
dc.contributor.author Yildirim, P.
dc.date.accessioned 2024-05-25T11:18:04Z
dc.date.available 2024-05-25T11:18:04Z
dc.date.issued 2015
dc.department Okan University en_US
dc.department-temp [Yildirim, P.] Okan Univ, Fac Engn & Architecture, Dept Comp Engn, Istanbul, Turkey en_US
dc.description.abstract Background: Ciprofloxacin is one of the main drugs to treat bacterial infections. Bacterial infections can lead to high morbidity, mortality, and costs of treatment in the world. In this study, an analysis was conducted using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database on the adverse events of ciprofloxacin. Objectives: The aim of this study was to explore unknown associations among the adverse events of ciprofloxacin, patient demographics and adverse event outcomes. Methods: A search of FDA AERS reports was performed and some statistics was highlighted. The most frequent adverse events and event outcomes of ciprofloxacin were listed, age and gender specific distribution of adverse events are reported, then the apriori algorithm was applied to the dataset to obtain some association rules and objective measures were used to select interesting ones. Furthermore, the results were compared against classical data mining algorithms and discussed. Results: The search resulted in 6 531 reports. The reports included within the dataset consist of 3 585 (55.8%) female and 2 884 (44.1%) male patients. The mean age of patients is 54.59 years. Preschool child, middle aged and aged groups have most adverse events reports in all groups. Pyrexia has the highest frequency with ciprofloxacin, followed by pain, diarrhoea, and anxiety in this order and the most frequent adverse event outcome is hospitalization. Age and gender based differences in the events in patients were found. In addition, some of the interesting associations obtained from the Apriori algorithm include not only psychiatric disorders but specifically their manifestation in specific gender groups. Conclusions: The FDA AERS offers an important data resource to identify new or unknown adverse events of drugs in the biomedical domain. The results that were obtained in this study can provide valuable information for medical researchers and decision makers at the pharmaceutical research field. en_US
dc.identifier.citationcount 19
dc.identifier.doi 10.4338/ACI-2015-06-RA-0076
dc.identifier.endpage 747 en_US
dc.identifier.issn 1869-0327
dc.identifier.issue 4 en_US
dc.identifier.pmid 26763627
dc.identifier.scopus 2-s2.0-84949955166
dc.identifier.scopusquality Q2
dc.identifier.startpage 728 en_US
dc.identifier.uri https://doi.org/10.4338/ACI-2015-06-RA-0076
dc.identifier.uri https://hdl.handle.net/20.500.14517/289
dc.identifier.volume 6 en_US
dc.identifier.wos WOS:000367349000011
dc.identifier.wosquality Q3
dc.language.iso en
dc.publisher Georg Thieme verlag Kg en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 19
dc.subject Data processing en_US
dc.subject adverse drug event en_US
dc.subject clinical decision support en_US
dc.subject clinical informatics en_US
dc.subject clinical care en_US
dc.title Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events en_US
dc.type Article en_US
dc.wos.citedbyCount 20
dspace.entity.type Publication

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