can-machine-learning-complement-traditional-medical-device-surveillanc-peer-reviewed-article-MDER?utm_content=buffer1cc7f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

Methods and results: 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 for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. This work is published and licensed by Dove Medical Press Limited. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.