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相关概念视频

Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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自然语言处理用于从病理报告中提取SNOMED-CT代码.

Giorgio Cazzaniga1, Albino Eccher2, Enrico Munari3

  • 1Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy.

Pathologica
|January 5, 2024
PubMed
概括

人工智能 (AI) 工具可以自动用SNOMED-CT代码标记病理报告,改善数据组织. 自然语言处理 (NLP) 方法,如支持矢量机 (SVM),证明了对叙事报告的有效编码.

关键词:
这里是SNOMED-CT.数字病理学数字病理学实验室信息系统实验室信息系统自然语言处理自然语言处理.

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科学领域:

  • 数字病理学数字病理学
  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 标准化结构报告 (SSR) 和SNOMED-CT等术语对于病理学数据检索和分析至关重要.
  • 叙事报告的普遍性阻碍了大规模的研究和协作,需要自动化标签解决方案.

研究的目的:

  • 开发和评估自然语言处理 (NLP) 方法,自动将SNOMED-CT代码与数字病理学报告联系起来.
  • 解决在病理档案中组织非结构化的叙事报告的挑战.

主要方法:

  • 两种基于NLP的自动编码系统,支持矢量机 (SVM) 和长短期记忆 (LSTM) 被训练并应用于叙事病理报告.
  • 整合了可解释性功能,以识别重要术语并优化模型性能.

主要成果:

  • 在1163个案例中,SVM和LSTM模型都实现了良好的性能指标 (准确性,精度,回忆,F1得分).
  • 与LSTM模型相比,SVM模型的性能略高.
  • 可解释性通过强调关键术语和词组,促进了模型的微调.

结论:

  • 人工智能工具,特别是NLP,可以自动化SNOMED-CT病理档案的标签.
  • 这种方法为叙事病理学报告中缺乏组织提供了回顾性解决方案.
  • 自动编码提高了数据的可访问性,并支持未来的研究和合作.