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Updated: Jan 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于机器学习的初级膜性病患者静脉血栓塞栓风险预测模型的开发

Lian Li1,2, Liuyun Wu1, Yin Wang1

  • 1Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Frontiers in pharmacology
|November 24, 2025
PubMed
概括

这项研究开发了一种机器学习模型,用于预测一次性膜性病 (PMN) 患者的静脉血栓栓塞 (VTE) 风险. 该模型有助于个性化抗凝剂治疗决策,以改善患者的治疗结果.

关键词:
机器学习是机器学习.预测模型 预测模型原发性膜性脏病 - 一次性膜性脏病.风险因素 风险因素静脉血栓塞栓症是一种静脉血栓塞栓.

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