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相关概念视频

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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相关实验视频

Updated: Jun 17, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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ICDXML:通过概率标签树和动态语义表示来增强ICD编码.

Zeqiang Wang1,2, Yuqi Wang1,3, Haiyang Zhang1

  • 1Department of Computing, Xi'an Jiaotong Liverpool University, Suzhou, 21500, China.

Scientific reports
|August 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用先进的人工智能编码国际疾病分类 (ICD) 的新模型. 它通过利用多式模式学习和层次结构来提高从医学文本中准确的临床代码赋值.

关键词:
极端的多标签分类.有几次射击学习学习.ICD编码是指ICD的编码.医学知识代表性的医学知识代表.自然语言处理自然语言处理.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 准确的临床编码对于医疗保健应用至关重要.
  • 医疗语言的复杂性给自动诊断和程序编码带来了挑战.
  • 现有的方法在临床文本的细微差别上扎,以准确地分配国际疾病分类 (ICD).

研究的目的:

  • 提出一种新型模型,以从临床文本中增强国际疾病分类 (ICD) 编码.
  • 提高自动化临床代码分配的准确性和稳定性.
  • 利用多模式学习和层次结构来实现更丰富的语义编码.

主要方法:

  • 开发了一个模型,用于ICD编码的极端多标签分类.
  • 利用可变形的卷积神经网络,从预先训练的语言模型中融合表示.
  • 综合外部医疗知识嵌入使用多模式方法.
  • 基于ICD的层次结构构建了一个概率标签树,用于结构化预测.

主要成果:

  • 拟议的模型在医疗代码预测任务中表现出了竞争力.
  • 在广泛使用的临床数据集MIMIC-III数据库上进行了实验.
  • 语言模型表示和医学知识嵌入的融合被证明是有效的.

结论:

  • 这种新型模型在强大的临床代码分配方面取得了重大进展.
  • 结合极端的多标签分类和层次结构可以提高ICD编码的准确性.
  • 这种技术有望改善依赖于精确临床文档的医疗保健应用.