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Updated: Jan 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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多个研究R-learner用于估计跨研究的异质治疗效应,使用统计机器学习

Cathy Shyr1, Boyu Ren2, Prasad Patil3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, United States.

Biostatistics (Oxford, England)
|December 18, 2025
PubMed
概括

估计异质治疗效应 (HTE) 对精准医学至关重要. 我们的多项研究R-learner框架有效地利用多项研究来改善HTE估计,特别是当治疗分配在研究之间有所不同时.

关键词:
研究之间的异质性研究之间的异质性有关因果推理的推理.有条件的平均治疗效果.机器学习是机器学习.精准医学是一门精准医学.

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