首页 > 过刊浏览>2022年第43卷第1期 >基于无监督深度学习的电子健康档案数据挖掘技术研究进展
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基于无监督深度学习的电子健康档案数据挖掘技术研究进展
作者:
作者单位:

(中国医学科学院/北京协和医学院医学信息研究所 北京 100020)

中图分类号:

R-058

基金项目:

北京市自然科学基金重点项目(项目编号:Z200016);中国医学科学院医学与健康科技创新工程“医学人工智能算法评价标准库构建”(项目编号:2018-I2M-AI-016)。


Research Progress of Electronic Health Record Data Mining Based on Unsupervised Deep Learning
Author:
Affiliation:

Institute of Medical Information, Chinese Academy of Medical Sciences ＆ Peking Union Medical College, Beijing 100020, China

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    摘要:

    介绍自编码器、生成式对抗网络、BERT等无监督深度学习方法,阐述其在电子健康档案数据挖掘中的应用以及存在的挑战,指出无监督深度学习技术能够加速医疗知识发现和临床决策支持,促进个性化医学发展。

    Abstract:

    The paper introduces unsupervised deep learning methods such as autoencoder, Generative Adversarial Network (GAN) and BERT and their applications and challenges in Electronic Health Record (EHR) data mining, and points out that unsupervised deep learning technology improves the efficiency of medical knowledge discovery and clinical decision support and promotes the development of personalized medicine.

    参考文献
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顾耀文,李姣.基于无监督深度学习的电子健康档案数据挖掘技术研究进展[J].医学信息学杂志,2022,43(1):34-40

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  • 在线发布日期: 2022-03-09

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