Models to Predict Survival after Liver Transplantation

Abstract

In light of the growing scarcity of livers available for transplantation, careful decisions must be made in organ allocation. The current standard of care for transplant decision making is the use of clinical judgment, although a good model to predict survival after liver transplantation may be useful to support these difficult decisions. This thesis explores the use of informatics techniques to improve upon past research in modeling liver transplant survival. A systematic literature revealed that the use of machine learning techniques has not been thoroughly explored in the field. Several experiments examined different modeling techniques using a database from the United Network for Organ Sharing. A Bayesian network was created to predict survival after liver transplantation, and it exceeded the performance of other models published in the literature. Fully automated feature selection techniques were used to identify the key predictors of liver transplant survival in a large database. A support vector machine was used to show that a relatively simple model, consisting of main effects and two-way interactions, may be adequate for predicting liver transplant survival. A pilot study was conducted to assess the ability of expert clinicians in predicting survival, and they tended to perform similarly to mathematical models. The results lay a foundation for future refinements in survival modeling and for a clinical trial of decision support in liver transplantation.