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Updated: Jun 13, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 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
|September 8, 2024
PubMed
概括

一个新的算法,hdiCF,使用索赔数据识别出具有不同治疗反应的患者子组. 这种方法通过发现异质治疗效应 (HTEs) 的重要特征来改进现有的机器学习.

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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