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相关实验视频

Updated: Jun 14, 2025

Monitoring Acupuncture Effects on Human Brain by fMRI
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Monitoring Acupuncture Effects on Human Brain by fMRI

Published on: April 8, 2010

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针指示知识库:基于ACUBERT的午线实体识别和分类.

TianCheng Xu1,2, Jing Wen1,2, Lei Wang3

  • 1Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.

Database : the journal of biological databases and curation
|August 30, 2024
PubMed
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艾库伯特模型增强了针指示中的午线分类,优于其他模型. 这种深度学习方法提高了标准化传统中医疗治疗的准确性.

科学领域:

  • 传统中国医药 传统中国医药
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 针中的非定量描述限制了标准化的治疗方法.
  • 准确的午线分类对于诊断和治疗至关重要.

研究的目的:

  • 评估针双向编码器表示从变压器 (ACUBERT) 模型的有效性,以识别和分类午线实体.
  • 为了解决针指示中的午线分类中的差异.
  • 通过深度学习开发一种标准化的针治疗方法.

主要方法:

  • 开发了基于BERT架构的ACUBERT模型.
  • 利用82本针医学书籍中的54,593个实体的预训机构.
  • 训练了使用八项原理和-差异化的午线差异化模型.
  • 将ACUBERT与支向量机和随机森林模型进行比较.

主要成果:

  • 与基线模型相比,ACUBERT 证明了更高的分类有效性.
  • 该模型在第五个时代实现了最佳性能,精度,回忆和F1分数超过0.8.8.
  • 建立了一个针指示知识库 (ACU-IKD) 和ACUBERT模型.

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相关实验视频

Last Updated: Jun 14, 2025

Monitoring Acupuncture Effects on Human Brain by fMRI
09:55

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Published on: April 8, 2010

15.4K
Visualizing Motion Patterns in Acupuncture Manipulation
08:18

Visualizing Motion Patterns in Acupuncture Manipulation

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Author Spotlight: Exploring Acupuncture in Alzheimer's Research from Thread-Embedding Techniques to Clinical Trials
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结论:

  • 艾库伯特模型显著提高了针指示中的午线归因的分类准确性.
  • 基于BERT的深度学习方法在传统中医中为多类,大规模的文本分类提供了优势.
  • 这项研究有助于标准化针诊断和治疗方法.