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.

Graphic Abstract

中文翻译：

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基于迁移学习的肝移植术后并发症计算机辅助诊断

摘要

肝移植是治疗急性肝衰竭、肝硬化甚至肝癌最有效的治疗方法之一。术后并发症的预测对于肝移植具有重要意义。然而，由于真实肝移植数据量不足，现有基于传统机器学习的预测方法往往不可用或不可靠。因此，我们提出了一种新的框架，通过迁移学习来提高肝移植术后并发症的计算机辅助诊断的准确性，该框架可以处理小规模但高维的数据问题。此外，由于现实世界中的数据样本通常是高维的，捕获影响术后并发症的关键特征可以帮助对患者做出正确的诊断。因此，我们还将 SHapley Additive exPlanation (SHAP) 方法引入我们的框架中，以探索术后并发症的关键特征。我们在实验中使用了从 425 名患者获得的具有 456 个特征的数据。实验结果表明，我们的方法在预测术后并发症方面优于所有比较的基线方法。在我们的工作中，平均精度、平均召回率和平均 F1 分数分别达到 91.22%、91.70% 和 91.18%。

图文摘要