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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedRoss JS, Bates J, Parzynski CS
Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.
Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, the researchers applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models. The three approaches, including one machine learning method, identified important safety signals, but without exact agreement.
AHRQ-funded; HS023000.
Citation: Ross JS, Bates J, Parzynski CS .
Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.
Med Devices 2017 Aug 16;10:165-88. doi: 10.2147/mder.s138158.
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Keywords: Medical Devices, Registries, Patient Safety, Adverse Events