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肝移植供体-接受者匹配与线下强化学习

Andrew Melehy¹, Jeffrey Feng², Dominic Amara²

¹University of California Los Angeles, Los Angeles, CA, USA. amelehy@mednet.ucla.edu.

NPJ digital medicine

|March 17, 2026

概括

强化学习优化了肝移植 (LT) 决策,将移植失败率降低了73%,并保持了93%的成功移植. 这种方法改善了等待肝移植的患者的捐赠者-接受者匹配.

科学领域:

医疗信息学医疗信息学
机器学习在医学中的应用
移植手术移植手术

背景情况:

肝移植 (LT) 面临着由于等候名单死亡率高,器官稀缺以及移植失败风险的挑战.
目前的方法静态地预测成功的捐赠者-接受者对,忽视了随着时间的推移的动态风险.
优化LT决策需要平衡等候名单死亡率与移植失败风险.

研究的目的:

应用离线强化学习 (RL) 方法来优化肝移植中的顺序决策.
根据不断变化的风险,动态管理患者等待名单状态 (等待,删除名单,移植).
改善捐赠者-接受者匹配和LT患者的治疗结果.

主要方法:

利用线下强化学习 (RL) 框架来模拟LT的决策.
训练RL模型使用来自移植受体科学注册 (SRTR) 数据库的历史等候名单数据.
代表了 LT 过程作为一系列依赖时间的决策:等待,删除或移植.

主要成果:

RL模型避免了73%的捐赠者-接受者对导致移植失败或死亡.
成功保存了93%的移植,导致了积极的结果.
在等待名单上死亡的47%患者中确定了潜在的合适捐赠者.

结论:

基于RL的方法为肝移植捐赠者与接受者匹配提供了更为动态和现实的模型.
该模型学习了成功的捐赠者-接受者对的关键特征,增强了临床决策支持.
这项研究表明RL作为改善LT结果的有价值的临床工具的潜力.

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