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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 2 of 2 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
Zhu Y, Chen CY, Matsuyama Y
Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers.
The researchers aimed to assess the validity of probabilistic linkage in the absence of unique personal identifiers (UPI) and the methods of cutoff weight selection. They found that probabilistic linkage without UPI generated valid linkages when an optimal cutoff was chosen and concluded that histogram inspection, the duplicate method, and the odds formula method can be used in conjunction when a gold standard is not available.
AHRQ-funded; 29020050016I.
Citation: Zhu Y, Chen CY, Matsuyama Y .
Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers.
Pharmacoepidemiol Drug Saf 2016 Apr;25(4):444-52. doi: 10.1002/pds.3832.
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Keywords: Data, Medical Devices, Registries