Use of neural networks for prediction of graft failure following liver transplantation

Abstract

Liver transplantation k a well-established therapeutic option forpatients with end-stage liver dkease. However, up to 20% of transplanted livers fail to have adequate function initially, and at least harf of those will eventually fail. Accurate, early prediction of outcome may ameliorate thk situation by encouraging retransplantation before the patient's condition becomes irreversible.In thk shrdy, clinical information was gathered prospectively for 295 patients who underwent liver transplantation at the Universiy of Pittsburgh Medical Center, and was divided into sets. The feed-forward, fully connected, neural networks had 7 or 8 inputs, a single hidden layer conskting of 3 nodes and a single output node cfailure=I, success=O). The networks were trained with data from a randomly selected subset of 240 patients while the remaining 55 patients made up the test set. The preoperative (day 0) data conskted of patient demographics plus the results of standard liver function tests. The "day 1" data consisted information gathered during surgery plus the prediction of outcome from day 0. Data for days 2-5 included resultsfrom standard liver function tests plus the prediction of outcome from the previous day's network The network was trained using a standard back propagation algorithm. naining was assessed by testing the abiliy of the network to correctly predict the outcome of the 55 patients in the test set. The accuracy of prediction by the neural network improved each day and so by day 5, 98% of the grafi survivors in the test set were correctly predicted while 88% of grafi failures in the test set were correctly predicted.

A Neural Network Approach to Predict Acute Allograft Rejection in Liver Transplant Recipients Using Routine Laboratory Data

“…In the liver transplantation field, the use of ANNs has been scrutinized in prediction of fibrosis in hepatitis C virus infected liver transplant recipients (12), prediction of outcomes after liver transplantation (21), and prediction of graft failure (22). Furthermore, ANNs have been designed to predict patient survival after liver transplantation (35) and early transplant failure (36).…”

“…ANNs as a datamodeling method have been studied in different areas of medical applications (3,13,14,(17)(18)(19)(20). In the transplantation field, the use of ANNs has been considered in the prediction of fibrosis in hepatitis C virus infected liver transplant recipients (12), prediction of outcomes after liver transplantation (21), and prediction of graft failure (22). Hence, liver transplantation is a field that would largely benefit from such models.…”

Comparison of Artificial Neural Networks with Logistic Regression in Prediction of Kidney Transplant Outcomes

“…ANN have the ability to provide good solutions in situations where large number of variables contribute to an outcome but their individual influence is weak and not well understood. Clinical data gathered from patients who have undergone graft transplant surgery have this characteristic and are known to be complex [8], [9]. In this paper we compare a widely used ANN approach known as Multi-layer Perceptron (MLP) networks with logistic regression, with the challenge being to select the right kidney from the available pool of organs for a particular patient, thereby maximizing the chances for the successful transplantation.…”

Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients

“…In addition, the use of ANNs was investigated in the prediction of graft failure [18], in the prediction of liver transplantation outcome [11], in the selection of patients for liver transplantation [19] and in the prediction of tacrolimus blood levels [8]. However, these studies used static models to predict long-term outcomes based on data collected before and immediately after transplantation.…”