Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of...

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Dades bibliogràfiques

Autors principals:	Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2021-01-01
Col·lecció:	IEEE Access
Matèries:	Survival prediction class imbalance missing data imputation decision-making liver transplantation generative adversarial networks
Accés en línia:	https://ieeexplore.ieee.org/document/9432931/
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