Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features

Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features

Abstract Background Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. P...

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Detaylı Bibliyografya
Asıl Yazarlar: Anamul Haque, Doug Stubbs, Nina C. Hubig, Francis G. Spinale, William J. Richardson
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: BMC 2022-10-01
Seri Bilgileri:BMC Medical Informatics and Decision Making
Konular:
Interpretable machine learning
Heart failure
Biomarkers
Cardiac resynchronization therapy
Personalized medicine
Online Erişim:https://doi.org/10.1186/s12911-022-02015-0

Internet

https://doi.org/10.1186/s12911-022-02015-0

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