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dc.contributor.authorKajungu, D K*
dc.contributor.authorErhart, A*
dc.contributor.authorTalisuna, A O*
dc.contributor.authorBassat, Q*
dc.contributor.authorKarema, C*
dc.contributor.authorNabasumba, C*
dc.contributor.authorNambozi, M*
dc.contributor.authorTinto, H*
dc.contributor.authorKremsner, P*
dc.contributor.authorMeremikwu, M*
dc.contributor.authorD'Alessandro, U*
dc.contributor.authorSpeybroeck, N*
dc.date.accessioned2014-06-04T07:42:06Z
dc.date.available2014-06-04T07:42:06Z
dc.date.issued2014-05
dc.identifier.citationPaediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven african countries. 2014, 9 (5):e96388 PLoS ONEen_GB
dc.identifier.issn1932-6203
dc.identifier.pmid24787710
dc.identifier.doi10.1371/journal.pone.0096388
dc.identifier.urihttp://hdl.handle.net/10144/318845
dc.description.abstractPharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children.
dc.language.isoenen
dc.publisherPublic Library of Scienceen_GB
dc.rightsPublished by Public Library of Science, [url]http://www.plosone.org/[/url] Archived on this site by Open Access permissionen_GB
dc.subjectMalariaen_GB
dc.subjectPediatricsen_GB
dc.titlePaediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven african countriesen
dc.identifier.journalPloS Oneen_GB
refterms.dateFOA2019-03-04T11:14:24Z
html.description.abstractPharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children.


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