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Computer-Aided Diagnosis of Complications After Liver Transplantation Based on Transfer Learning

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Liver transplantation is one of the most effective treatments for acute liver failure, cirrhosis, and even liver cancer. The prediction of postoperative complications is of great significance for liver transplantation. However, the existing prediction methods based on traditional machine learning are often unavailable or unreliable due to the insufficient amount of real liver transplantation data. Therefore, we propose a new framework to increase the accuracy of computer-aided diagnosis of complications after liver transplantation with transfer learning, which can handle small-scale but high-dimensional data problems. Furthermore, since data samples are often high dimensional in the real world, capturing key features that influence postoperative complications can help make the correct diagnosis for patients. So, we also introduce the SHapley Additive exPlanation (SHAP) method into our framework for exploring the key features of postoperative complications. We used data obtained from 425 patients with 456 features in our experiments. Experimental results show that our approach outperforms all compared baseline methods in predicting postoperative complications. In our work, the average precision, the mean recall, and the mean F1 score reach 91.22%, 91.70%, and 91.18%, respectively.

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Data availability

The source code is available at https://github.com/laugoon/Computer-Aided-Diagnosis-of-Complications-After-Liver-Transplantation-Based-on-Transfer-Learning.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (No.52078212), the Key Medical Professional Development Plan of Beijing Municipal Hospital Administration (No.ZYLX201822), and the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research under Grant IWHR-SKL-202003.

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Ying Zhang, Chenyuan Shangguan, Jiyuan He & Meng Jia

  2. Department of Anesthesiology Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100069, China

    Xuena Zhang

  3. Tianjin Zhuoman Technology Co., Ltd., Tianjin, 300000, China

    Jialin Ma

  4. Hebei Vocational College of Rail Transportation, Shijiazhuang, 050051, China

    Na Chen

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  1. Ying Zhang
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Correspondence to Ying Zhang.

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Appendix A Instance Example

Appendix A Instance Example

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Zhang, Y., Shangguan, C., Zhang, X. et al. Computer-Aided Diagnosis of Complications After Liver Transplantation Based on Transfer Learning. Interdiscip Sci Comput Life Sci 16, 123–140 (2024). https://doi.org/10.1007/s12539-023-00588-6

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