A predictive model for acute allograft rejection of liver transplantation

Orthotopic liver transplantation (OLT) has become an increasingly used treatment for end-stage liver disease. However, acute allograft rejection is still a problem in postoperative care of liver transplantation with immunosuppressive therapy and it can lead to allograft damage and harm the survival of liver transplantation patient. This work proposes to use data-driven approach to build a predictive model for acute rejection. We consider not only prediction accuracy, but also interpretability of the prediction outcome in building the predictive model, so that the medical staffs can identify how the prediction is induced from data. The experiments use the real data provided by liver transplantation intensive care unit (ICU) of Chang Gung Memorial Hospital, Taiwan. In this work, the data is from a medical center, in which the patient data ranges from 2004 to 2013, and the number of data records is approximately 2 million. To the best of our knowledge, this is the first work using a large-scale database to focus on liver transplantation and generate interpretable rules that could be used by medical staffs. We compare with several methods, including SVM, ANN and random forest, and the experimental results indicate that the proposed method is comparative, and provides interpretable results. Central to the proposed method is to consider interpretability, and the goal is to provide interpretable results for the medical staffs to make decisions. The proposed transformation algorithms belong to data-driven approaches, so they could be applied to other intelligent or expert systems. Moreover, the outcomes are presented in rule format, which could be used by medical staffs and other expert systems.