AIMC Topic: Heart Transplantation

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Improving prediction of heart transplantation outcome using deep learning techniques.

Scientific reports
The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantati...

Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for recipients. The in advance prediction of PGD can help the transplant physician better manage the risks ...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

The current state of artificial intelligence in cardiac transplantation.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The field of heart transplantation is a complex practice that combines both science and art to optimize the quality and quantity of an organ transplant recipient's life span. In the current age of Transplant Medicine there are many...