Rating organ failure via adverse events using data mining in the intensive care unit
Artificial Intelligence in Medicine
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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Abstract: Liver transplantation is a well-established therapeutic option for patients with end-stage liver disease. However, up to 20% of transplanted livers fail to have adequate function initially, and at least half of those will eventually fail. Accurate, early prediction of outcome may ameliorate this situation by encouraging retransplantation before the patient's condition becomes irreversible. In this study, clinical information was gathered prospectively for 295 patients who underwent liver transplantation at the University of Pittsburgh Medical Center, and was divided into sets. The feedforward, fully connected neural networks had 7 or 8 inputs, a single hidden layer consisting of 3 nodes and a single output node (failure=1, success=0). 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 network was trained using a standard backpropagation algorithm. Training was assessed by testing the ability 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 graft survivors in the test set were correctly predicted while 88% of graft failures in the test set were correctly predicted.