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Updated: Jun 24, 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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对于具有可解释机器学习的门诊患者,不满意度考虑的等待时间预测.

Jongkyung Shin1, Donggi Augustine Lee2, Juram Kim3

  • 1Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology, 50 Unist-gil, Eonyang-eup, Ulju-gun, 44919, Ulsan, Republic of Korea.

Health care management science
|June 1, 2024
PubMed
概括

这项研究引入了一个新的框架,可以准确预测门诊等待时间,防止低估和减少患者不满. 具有不对称损失函数和新型错误得分的机器学习模型提供了更好的等待时间估计和解释.

关键词:
不对称的损失函数的功能.可以解释的机器学习.门诊服务 门诊服务患者的不满症患者的不满.预测等待时间预测

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