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Updated: Jun 6, 2025

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

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DOD-BART:基于机器学习的剂量优化设计,通过贝叶斯增量回归树结合患者水平的预后因素

Yunqi Zhao1, Rachael Liu1, Jianchang Lin1

  • 1Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.

Journal of biopharmaceutical statistics
|November 29, 2024
PubMed
概括

这项研究引入了一种新的无I/II期临床试验设计,DOD-BART,使用贝叶斯附加回归树 (BART) 来改善瘤药物剂量优化. 该设计通过整合患者数据并适应新出现的结果,提高了确定最佳剂量的效率和准确性.

关键词:
贝叶斯增量回归树是贝叶斯的增量回归树.贝叶斯自动自适应设计是贝叶斯的自动自适应设计.剂量优化剂量优化剂量随机化剂量随机化不同质性的异质性机器学习是机器学习.预后因素 预后因素

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