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

• Published in: Interdisciplinary sciences : computational life sciences Vol. 16; no. 1; pp. 123 - 140
• Main Authors: Zhang, Ying, Shangguan, Chenyuan, Zhang, Xuena, Ma, Jialin, He, Jiyuan, Jia, Meng, Chen, Na
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Cirrhosis; Complications; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Datasets; Deep learning; Diagnosis; Health Sciences; Life Sciences; Liver cancer; Liver failure; Liver transplantation; Liver transplants; Machine learning; Mathematical and Computational Physics; Medical prognosis; Medicine; Methods; Original Research Article; Patients; Postoperative; Sample size; Sampling techniques; Statistics for Life Sciences; Support vector machines; Theoretical; Theoretical and Computational Chemistry; Transfer learning; Transplantation; Feature importance; Prediction of complications; Transfer learning; Liver transplantation
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-023-00588-6

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. Graphic Abstract