An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation
Mamoun T. Mardini,
Chen Bai,
Maisara Bledsoe
et al.
Abstract:BackgroundThe use of Intra-aortic Balloon Pump (IABP) and Impella devices as a bridge to heart transplantation (HTx) has increased significantly in recent times. This study aimed to create and validate an explainable machine learning (ML) model that can predict the failure of status two listings and identify the clinical features that significantly impact this outcome.MethodsWe used the UNOS registry database to identify HTx candidates listed as UNOS Status 2 between 2018 and 2022 and supported with either Imp… Show more
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