AI for Predicting Heart Transplant Outcomes
A recent review has highlighted the growing use of artificial intelligence (AI) in managing post-heart transplantation (HTx) and mechanical circulatory support (MCS) clinical care. This technology is particularly promising in the field of heart failure practice, where many decisions are often made based on expert opinions in the absence of high-quality data-driven evidence. The review specifically focused on studies that examined post-HTx care, including post-operative management and long-term outcomes. The most common data sources used for development, training, and validation of AI algorithms are the United for Organ Sharing (UNOS) and the International Society of Heart and Lung Transplantation (ISHLT) registry. The primary outcomes studied include 1-year mortality or recipients, survival time, dependence on chronic dialysis, and graft survival. The machine learning models used include neural networks, support vector machines, random forests, and gradient-boosted machines. The p...