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Updated: Sep 13, 2025

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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一个基于几次学习的框架,用于预测住院患者死亡率的可能性

Josh Jia-Ching Ying1, Jia-Xuan Yu1, Chang-Lun Huang2

  • 1Department of Management Information Systems, National Chung Hsing University, Taichung, 402 Taiwan, R.O.C.

Journal of healthcare informatics research
|July 29, 2025
PubMed
概括

这项研究引入了电子健康记录 (EHR) 的深度学习框架,以预测住院患者的死亡率和住院时间. 该模型显示了更好的回忆和F1分数,解决了EHR数据挑战.

关键词:
深度学习是一种深度学习.电子健康记录电子健康记录有几次射击学习学习.住院患者死亡率的可能性.多任务学习多任务学习

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