Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma

Table 1 Studies with artificial neural networks and logistic regression models for the prediction of survival of patients in the field of cirrhosis and liver transplantation

Ref.	Year	Model and endpoint
Doyle et al[26]	1992	10 feed forward back-propagation ANN model to predict LT survival
Marsh et al[27]	1997	ANN for survival analysis and time to recurrence of HCC after LT
Parmanto et al[28]	2001	Back-propagation through time ANN algorithm to predict outcomes after LT
Cucchetti et al[29]	2007	ANN for survival prognosis of patients with cirrhosis
Zhang et al[30]	2012	MLP model for predicting outcomes of patients with cirrhosis and compared the performance with MELD and SOFA scores
Cruz et al[31]	2013	Radial basis function ANNs using multi-objective evolutionary algorithm to match the donor-recipient pairs
Lee et al[52]	2018	Compared the performance of ML approaches (decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, MLP, and deep belief networks) with that of LR analysis to predict AKI after LT for cirrhosis and HCC (49%)
He et al[53]	2021	LR analysis as a conventional model, and random forest, support vector machine, classical decision tree, and conditional inference tree algorithms to predict AKI after LT for cirrhosis and HCC (40.7%)

ANN: Artificial neural network; LR: Logistic regression; LT: Liver transplantation; HCC: Hepatocellular carcinoma; MLP: Multilayer perceptron; MELD: Model for end-stage liver disease; SOFA: Sequential Organ Failure Assessment; AKI: Acute kidney injury.