ArticleApplied clinical informatics2015
Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- Association Discovery Approach in Healthcare Big Data to Identify Drug Safety and Drug Repurposing Signals.Clinical pharmacology and therapeutics · 2026Article
- Deleterious effects of a combination therapy using fluoroquinolones and tetracyclines for the treatment of Japanese spotted fever: a retrospective cohort study based on a Japanese hospital database.The Journal of antimicrobial chemotherapy · 2024Article
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- Evaluation of neuroleptic malignant syndrome induced by antipsychotic drugs using spontaneous reporting system.Heliyon · 2023Article
- Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data.Drug safety · 2023Article
- Systemic quinolones and risk of acute liver failure I: Analysis of data from the US FDA adverse event reporting system.JGH open : an open access journal of gastroenterology and hepatology · 2021Article
- Analysis of immune-related adverse events caused by immune checkpoint inhibitors using the Japanese Adverse Drug Event Report database.Pharmacoepidemiology and drug safety · 2020Article
- Analysis of drug-induced interstitial lung disease using the Japanese Adverse Drug Event Report database.SAGE open medicine · 2020Article
- Mining Drugs and Indications for Suicide-Related Adverse Events.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Analysis of drug-induced hearing loss by using a spontaneous reporting system database.PloS one · 2019Article
- Analysis of fall-related adverse events among older adults using the Japanese Adverse Drug Event Report (JADER) database.Journal of pharmaceutical health care and sciences · 2018Article
- Analysis of adverse events of renal impairment related to platinum-based compounds using the Japanese Adverse Drug Event Report database.SAGE open medicine · 2018Article
- Drug-induced gingival hyperplasia: a retrospective study using spontaneous reporting system databases.Journal of pharmaceutical health care and sciences · 2017Article
- Thromboembolic adverse event study of combined estrogen-progestin preparations using Japanese Adverse Drug Event Report database.PloS one · 2017Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCiprofloxacin 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.
objectivesThe aim of this study was to explore unknown associations among the adverse events of ciprofloxacin, patient demographics and adverse event outcomes.
methodsA 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.
resultsThe 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.