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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Principais autores:	Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2021-01-01
סדרה:	IEEE Access
נושאים:	Survival prediction class imbalance missing data imputation decision-making liver transplantation generative adversarial networks
גישה מקוונת:	https://ieeexplore.ieee.org/document/9432931/
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