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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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相关实验视频

Updated: May 27, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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FHIR 颗粒式敏感数据细分技术

Preston Lee1,2, Daniel Mendoza1, Martha Kaiser1

  • 1Arizona State University, College of Health Solutions, Phoenix, Arizona, United States.

Applied clinical informatics
|February 19, 2025
PubMed
概括
此摘要是机器生成的。

通过新的尊重同意的技术,患者可以控制敏感的健康数据共享. 该系统使用先进的健康7级 (HL7) 标准进行细分数据细分,改善隐私和医生对齐.

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗记录管理 医疗记录管理
  • 健康数据 隐私数据 隐私数据

背景情况:

  • 由于耻辱感,患者希望加强对敏感医疗记录共享的控制.
  • 目前的技术需要更新到新的标准,以更好地与医生对敏感数据的分类保持一致.

研究的目的:

  • 部署和试点测试基于开源快速医疗互操作资源 (FHIR) 的数据细分技术.
  • 让医生参与设计一个决策引擎,支持数据共享的各种安全级别.

主要方法:

  • 开发了一个基于Web的患者门户网站和临床决策支持 (CDS) 颗粒数据细分引擎.
  • 使用FHIR R5,同意资源类型和CDS Hooks进行数据敏感性标记和编辑.
  • 实现了微妙数据分类的可配置置信值截止值.

主要成果:

  • 部署了一个系统,使患者能够基于同意的细粒度数据选择敏感信息,如物质使用记录.
  • 该引擎支持先进的HL7标准,超越了二进制分类,反映了医生信心水平.
  • 工程选择优先考虑了技术的适应性,可重复使用性和可扩展性.

结论:

  • 开发的数据细分技术将现有软件更新为最新的HL7标准.
  • 该系统更好地反映了医生如何使用不同的保证级别对敏感医疗信息进行分类.
  • 开源代码通过HL7 FHIR Foundry共享,以促进可重复使用性.