机器学习在肝移植中的应用

机器学习可以高效地从复杂的数据库中提取特征和建立联系，通过构建模型等方式更好地预测临床疾病变化。肝移植是治疗各种终末期肝病以及原发性肝癌（肝癌）的有效方法之一，但同时也面临许多挑战，如何更有效地进行器官分配、扩大供肝来源、评估最佳供受者匹配、预测移植术后并发症、疾病复发及远期生存一直是研究的热点和难点。近年来，机器学习在肝移植领域中的应用亦取得了一些成果，显示出巨大的前景。本文就机器学习在肝移植术前器官分配、供肝评估，围手术期并发症预测、输血预测，术后新发疾病预测、疾病复发预测、急性排斥反应预测及远期生存预后预测中的应用现状及前景做一综述，以期为后续的研究提供思路和方向。

Application of machine learning in liver transplantation

Machine learning can efficiently extract the features and establish associations from complex databases, and better predict changes in clinical diseases by constructing models. Liver transplantation is one of the efficacious therapeutic options for all types of end-stage liver diseases and primary liver cancer. Nevertheless, it also faces multiple challenges. How to more effectively allocate the organs, expand the donor liver pool, evaluate the optimal donor-recipient matching, predict the complications after liver transplantation, disease recurrence and long-term survival have been the hot spots and difficulties. In recent years, certain progress has been made in the application of machine learning in the field of liver transplantation, showcasing promising prospect. In this article, the application status and prospect of machine learning in organ allocation before liver transplantation, donor liver evaluation, prediction of perioperative complications, blood transfusion, postoperative new disease, disease recurrence, acute rejection and long-term survival were reviewed, aiming to provide ideas and direction for subsequent investigations.