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Updated: Jun 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Published on: October 10, 2018

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使用文档术语矩阵和XGBoost的药物诱导肝损伤的自动文本分类

Minjun Chen1, Yue Wu1, Byron Wingerd2

  • 1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.

Frontiers in artificial intelligence
|June 18, 2024
PubMed
概括

这项研究使用人工智能从药物标签上对药物诱导的肝损伤 (DILI) 进行分类,达到超过0.90 AUC. 这种自动化的文本分类有助于药物监督,并节省了审查员的资源.

关键词:
在 CatBoost 中使用 CatBoost.轻GBMM 轻GBM 轻GBM 轻GBM马修斯相关系数 (MCC) 是一个相关系数.国际货币基金组织-国际货币基金组织在XGBoost中使用.解剖学治疗化学分类 (ATC)在曲线下面的面积.

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