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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Detaylı Bibliyografya
Asıl Yazarlar:	Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2021-01-01
Seri Bilgileri:	IEEE Access
Konular:	Survival prediction class imbalance missing data imputation decision-making liver transplantation generative adversarial networks
Online Erişim:	https://ieeexplore.ieee.org/document/9432931/
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Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

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