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Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system
dc.contributor.authorZhao, Bangyao
dc.contributor.authorZhao, Lili
dc.date.accessioned2023-05-01T19:11:34Z
dc.date.available2024-06-01 15:11:32en
dc.date.available2023-05-01T19:11:34Z
dc.date.issued2023-05-10
dc.identifier.citationZhao, Bangyao; Zhao, Lili (2023). "Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system." Statistics in Medicine 42(10): 1512-1524.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/176293
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otheradverse event ontology
dc.subject.otherzero-inflated negative binomial distribution
dc.subject.otherVAERS
dc.subject.othervaccine adverse event
dc.subject.otherempirical Bayes
dc.titleMining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176293/1/sim9684_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176293/2/sim9684.pdf
dc.identifier.doi10.1002/sim.9684
dc.identifier.sourceStatistics in Medicine
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dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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