Can machine learning complement traditional medical device surveillance? A case study of dual-chambe...
Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators
About this item
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Author / Creator
Ross, Joseph S , Bates, Jonathan , Parzynski, Craig S , Akar, Joseph G , Curtis, Jeptha P , Desai, Nihar R , Freeman, James V , Gamble, Ginger M , Kuntz, Richard , Li, Shu-Xia , Marinac-Dabic, Danica , Masoudi, Frederick A , Normand, Sharon-Lise T , Ranasinghe, Isuru , Shaw, Richard E and Krumholz, Harlan M
Publisher
New Zealand: Dove Medical Press Limited
Journal title
Language
English
Formats
Publication information
Publisher
New Zealand: Dove Medical Press Limited
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More information
Scope and Contents
Contents
Machine learning methods may complement traditional analytic methods for medical device surveillance.
Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection...
Alternative Titles
Full title
Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_80a0e372e0914f688f94ecb1c3eed1bf
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_80a0e372e0914f688f94ecb1c3eed1bf
Other Identifiers
ISSN
1179-1470
E-ISSN
1179-1470
DOI
10.2147/MDER.S138158
