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Updated: Jun 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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高维代因果森林 (hdiCF) 用于使用医疗保健索赔数据识别子组

Tiansheng Wang1, Virginia Pate1, Richard Wyss2

  • 1Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.

American journal of epidemiology
|June 13, 2025
PubMed
概括

一种新的高维方法有效地确定了受益于特定治疗的患者子组,在检测心力衰竭风险的异质治疗效应方面表现优于标准方法. 这种方法有助于个性化医疗,通过精确确定最佳患者群体.

关键词:
代性因果森林是一种代性因果森林.因果机器学习是因果机器学习.索赔数据 索赔数据 索赔数据不同质的治疗效果.这是一个高维的高维空间.药学流行病学 药学流行病学精准医学是一门精准医学.小组的标识子组的标识

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