Artificial intelligence and kidney transplantation

Table 2 Machine learning applications used in different kidney transplantation areas

Kidney transplantation category	Machine learning methods used	Ref.
Radiological evaluation	Neural network, convolutional neural network, stacked autoencoders, Bayesian supervised classifier	[4-9]
Pathological evaluation	Neural network, Bayesian network, convolutional neural network, linear discriminant analysis, support vector machines, random forest, archetypal analysis	[10-23]
Prediction of graft survival	Neural network, logistic regression, decision tree, random forest, support vector machines, LASSO, gradient boosting	[24-39]
Optimizing the dose of immunosuppression	Neural network (multilayer perceptron, finite impulse response network, and the Elman recurrent network), adaptive-network-based fuzzy inference system, conditional inference trees, multiple linear regression, regression tree, multivariate adaptive regression splines, boosted regression tree, support vector regression, random forest regression, LASSO regression and Bayesian additive regression trees	[40-46]
Diagnosis of rejection	Neural network, support vector machines, Bayesian interference	[47-52]
Prediction of early graft function	Neural network, logistic regression, linear discriminant analysis, quadratic discriminant analysis, support vector machines, decision tree, random forest, gradient boosting, elastic net	[3,53-57]

LASSO: Least absolute shrinkage and selection operator.