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相关概念视频

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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相关实验视频

Updated: Jul 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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超级图形卷积神经网络与对比学习用于自动化ICD编码.

Yuzhou Wu1, Xuechen Chen2, Xin Yao2

  • 1the School of Computer Science and Engineering, Central South University, Changsha, 410012, China; China Mobile (Chengdu) Industrial Research Institute, Chengdu, 610041, China.

Computers in biology and medicine
|December 3, 2023
PubMed
概括

本研究介绍了HGCN-CL,这是一种用于自动化国际疾病分类 (ICD) 编码的新型深度学习模型. 该模型有效地解决了数据不平衡和代码层次等挑战,优于以前的方法.

关键词:
自动ICD编码自动化ICD编码代码层次结构的代码.相反的学习学习.不平衡的标签分发方式疾病的国际分类.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 手动国际疾病分类 (ICD) 编码是耗时且昂贵的.
  • 使用深度学习的自动化ICD编码面临诸多挑战,包括不平衡的标签分布,代码层次和噪音较大的文本.
  • 现有的方法难以有效和冗余的标签表示,特别是关于代码层次的代码层次.

研究的目的:

  • 为自动化ICD编码引入一种新的超级图形卷积网络与对比学习 (HGCN-CL) 模型.
  • 为了解决处理不平衡的标签分配和代码等级的先前方法的局限性.
  • 提高自动化ICD编码的准确性和效率.

主要方法:

  • 使用过度图形卷积网络 (HGCN) 有效地捕获ICD代码的层次结构,减轻嵌入扭曲.
  • 通过将代码特征注入到文本编码器中来生成具有层次意识的积极样本,集成了对比式学习.
  • 在公开的MIMIC-III和MIMIC-II数据集上训练和评估HGCN-CL模型.

主要成果:

  • 在MIMIC III数据集上,HGCN-CL模型与最先进的方法相比,表现优越.
  • 在自动化ICD编码方面,与之前的最佳结果 (Hypercore) 相比,实现了2.7%和3.6%的改进.
  • 废弃实验和层次可视化证实了模型组件的有效性.

结论:

  • 拟议的HGCN-CL模型为自动化ICD编码提供了显著的进步.
  • 该模型利用过度几何学和对比学习的能力有效地解决了该领域的关键挑战.
  • HGCN-CL为疾病分类和医疗数据分析提供了更准确,更有效的解决方案.