一个神经网络集成加速失效基于时间的混合治愈模型
Wisdom Aselisewine1, Suvra Pal1,2
1Department of Mathematics, University of Texas at Arlington, Arlington, TX 76019, United States.
这项研究引入了一种新的混合治愈率模型 (MCM),使用神经网络来确定治愈概率,在生存分析中表现优于传统方法,并提高了癌症患者的预测准确性.
科学领域:
- 生物统计学
- 机器学习
- 生存分析
背景情况:
- 混合治愈率模型 (MCM) 是治愈子组生存数据的标准.
- 传统的治疗概率建模使用逻辑链接的通用线性模型在捕捉复杂的共变量效应方面存在局限性.
研究的目的:
- 引入一种新型的MCM,其中包含一个神经网络分类器,用于治疗概率.
- 提高治疗概率估计的准确性和精确性以及生存分析的预测准确性.
主要方法:
- 开发了一种基于神经网络的治疗概率的新型MCM.
- 使用加速失效时间结构来计算未治愈患者的存活率.
- 使用预期最大化算法进行参数估计.
主要成果:
- 提出的基于神经网络的MCM在捕获非线性分类边界方面表现出卓越的表现.
- 在模拟中表现优于基于logit的MCM,基于spline的MCM和其他机器学习算法.
- 展示了改善的概率估计的准确性和精确性以及增强的预测准确性.
结论:
- 新的MCM有效地使用神经网络模拟复杂的治愈概率.
- 拟议的方法比现有的生存数据分析方法有显著的改进.
- 通过应用到白血病癌症患者的生存数据来证明其实用性.
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