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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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适应错误分类对Q学习优化动态治疗方案的影响.

Yasin Khadem Charvadeh1, Grace Y Yi1,2

  • 1Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Ontario, Canada.

Statistics in medicine
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概括
此摘要是机器生成的。

在动态治疗方案中忽略错误分类的共变量会损害Q学习. 本研究介绍了两种校正方法,以减轻参数估计中的偏差,以获得最佳决策规则.

关键词:
这就是Q-learning.动态处理方案 动态处理方案估计功能的估计功能这是错误的分类错误.回归校准的回归校准.回归模型是一种回归模型.

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 生物统计学 生物统计学

背景情况:

  • 动态治疗方案 (DTR) 对于个性化医疗至关重要.
  • 现有的DTR方法,包括Q学习,对错误分类的共变量敏感.
  • 同变量错误分类对Q学习的影响仍未得到充分研究.

研究的目的:

  • 调查忽视错误分类的二进制共变量的Q学习对DTR中最佳决策规则的影响.
  • 提出和评估在存在共变量错误分类的情况下纠正Q学习的方法.

主要方法:

  • 实证研究进行了,以证明错误分类对Q学习的影响.
  • 开发了两种新的校正方法来解决错误分类偏差.
  • 使用数值模拟来评估拟议方法的性能.

主要成果:

  • 忽视二进制共变量中的错误分类导致Q学习中的显著估计偏差.
  • 建议的校正方法有效地减少了估计偏差.
  • 通过校正方法,参数估计的准确性得到了提高.

结论:

  • 共同变量错误分类对DTR中的Q学习构成了重大挑战.
  • 开发的校正方法提供了一个可行的解决方案,以减轻偏差.
  • 准确的参数估计是可以实现的,即使使用错误分类的共变量使用提出的技术.