Browsing by Author "Soong, Ruey-Shyang"

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A predictive model for acute allograft rejection of liver transplantation
(2018-03-15)
Liu, Chien-Liang
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Soong, Ruey-Shyang
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Lee, Wei-Chen
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Chen, De-Hsuan
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Hsu, Shang-Hwa
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. (C) 2017 Elsevier Ltd. All rights reserved.
Predicting Short-term Survival after Liver Transplantation using Machine Learning
(2020-03-27)
Liu, Chien-Liang
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Soong, Ruey-Shyang
;
Lee, Wei-Chen
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Jiang, Guo-Wei
;
Lin, Yun-Chun
Liver transplantation is one of the most effective treatments for end-stage liver disease, but the demand for livers is much higher than the available donor livers. Model for End-stage Liver Disease (MELD) score is a commonly used approach to prioritize patients, but previous studies have indicated that MELD score may fail to predict well for the postoperative patients. This work proposes to use data-driven approach to devise a predictive model to predict postoperative survival within 30 days based on patient's preoperative physiological measurement values. We use random forest (RF) to select important features, including clinically used features and new features discovered from physiological measurement values. Moreover, we propose a new imputation method to deal with the problem of missing values and the results show that it outperforms the other alternatives. In the predictive model, we use patients' blood test data within 1-9 days before surgery to construct the model to predict postoperative patients' survival. The experimental results on a real data set indicate that RF outperforms the other alternatives. The experimental results on the temporal validation set show that our proposed model achieves area under the curve (AUC) of 0.771 and specificity of 0.815, showing superior discrimination power in predicting postoperative survival.