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Updated: Feb 1, 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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机器学习的死亡率预测模型用于儿童无性贫血中循环氨酸治疗

Xianhao Wen1, Li Xiao2, Danni Li1

  • 1Department of Hematology and Oncology, National Clinical Research Center for Children and Adolescents' Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Children's Hospital of Chongqing Medical University, Chongqing, China.

Annals of hematology
|January 30, 2026
PubMed
概括

一个新的机器学习模型预测了用环素治疗的无性贫血儿童的死亡风险. CatBoost模型,使用诸如网细胞计数等因素,改善了个性化治疗策略的风险分层.

关键词:
无形成性贫血是什么意思孩子 孩子 孩子 孩子循环氨酸是一种环素.机器学习 机器学习死亡率 死亡率 死亡率

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