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Updated: Jan 18, 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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预测肝硬化患者非计划性再入院风险:复杂感知动态分类器选择方法

Zixin Shi1, Linjun Huang1, Xiaomei Xu2

  • 1College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.

JMIR medical informatics
|September 10, 2025
PubMed
概括

这项研究引入了一个新的框架,用于使用电子健康记录 (EHR) 预测肝硬化再入院. 该方法通过为个别患者子组动态选择最佳机器学习模型来提高预测准确性,从而增强临床决策支持.

关键词:
欧洲人权理事会 欧洲人权理事会肝硬化是肝硬化的一种疾病.数据挖掘是数据挖掘的一个方法.在决策过程中做出决定.电子健康记录是电子健康记录.一个基本的框架框架.胃肠道疾病的疾病.多个分类系统的多个分类系统.预测模型的预测模型.再接收的风险 再接收的风险

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