在当代时代心脏移植后预测死亡率的生存机器学习方法
Lathan Liou1,2, Elizabeth Mostofsky1, Laura Lehman1,3,4
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
PloS one
|January 8, 2025
概括
这项研究对2018年政策变化后的心脏移植生存预测的机器学习模型进行了基准测试. 随机生存森林和考克斯梯度提升在预测1年死亡率方面表现最高.
科学领域:
- 心脏病学 心脏病学
- 移植医学 移植医学
- 医疗保健中的机器学习
背景情况:
- 之前的心脏移植结果预测模型已经存在.
- 需要对当代生存机器学习方法进行全面的基准测试,特别是在2018年捐赠心脏分配政策变化之后.
研究的目的:
- 在2018年后的政策变化后,对七种统计和机器学习算法进行基准测试,以预测成年心脏移植患者的死亡率.
- 为了比较政策后和季节匹配的政策前队列之间的模型性能.
主要方法:
- 利用移植受体科学注册 (SRTR) 数据库,对2018年10月18日或之后接受首次心脏移植的7,160名成年心脏移植受体进行了分析.
- 评估了拉索,,弹性网,考克斯梯度增强,极端梯度增强线性,极端梯度增强树和随机生存森林.
- 在mlr中使用交叉验证框架进行模型评估.
主要成果:
- 在政策后的队列中,随机生存森林 (C指数:0.628) 和考克斯梯度提升 (C指数:0.627) 显示了最高的预测性能.
- 群体之间的重要变量有所不同;例如,在政策后群体中注意到没有ECMO.
- 生存机器学习模型可以合理预测移植后1年死亡率.
结论:
- 生存机器学习模型为移植后的一年死亡率提供了宝贵的见解.
- 这些预测模型的持续更新对于心脏移植的当代时代至关重要.
- 2018年的政策变化影响了某些预测变量的相对重要性.
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