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Updated: Jun 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

从数据到决策:一种可解释的机器学习模型,用于优化Graves甲状腺功能障碍患者的RAI治疗

Lu Lu1, Xiaojuan Wei1, Yan Chen1

  • 1Department of Nuclear Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Autonomous Region, China.

Frontiers in endocrinology
|February 11, 2026
PubMed
概括

对格雷夫斯甲状腺功能障碍 (GH) 的放射性 (RAI) 治疗有显著的失败率. 机器学习模型,特别是随机森林,可以使用关键患者因素更准确地预测RAI结果,改进治疗策略.

关键词:
格雷夫斯的疾病.可以解释的人工智能AI机器学习是机器学习.精准医学是一门精准医学.放射性治疗是一种放射性治疗.预测治疗结果,预测治疗结果.

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