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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
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

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预测接受连续脏置换治疗的危急病患者的28天死亡率:一种新的可解释机器学习方法

Tao Zhang1, Zi-Han Nan1, Xiao-Xuan Fan1

  • 1Department of Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050000, People's Republic of China.

Journal of multidisciplinary healthcare
|September 11, 2025
PubMed
概括

一个可解释的机器学习模型准确地预测了接受连续脏替代疗法 (CRRT) 的危急病患者的28天死亡率. 该工具有助于早期风险分层和临床决策,以改善患者的治疗结果.

关键词:
斯过程是高斯过程.这是一个MIMIC-IV数据库.这就是 SHAP SHAP 的意思.危急的疾病危急的疾病.通过外部验证.可以解释的解释性.机器学习是机器学习.死亡率预测死亡率预测

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