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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

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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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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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优化光谱回归用于白细胞量化,使用元启发性特征选择.

Sonia Mustafa1, Gang Li1, Honghui Zeng2

  • 1Medical School of Tianjin University, Tianjin, 300072, China; State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China.

Computers in biology and medicine
|October 25, 2025
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概括

这项研究引入了一种优化的光谱方法,用于准确估计白细胞 (WBC) 数量. 生物知情子模型和元启发性优化显著提高了临床决策的WBC预测准确性.

关键词:
基于M+N理论的建模.超启发性特征选择选项最少侵入性的诊断方法频谱数据分析的数据分析.白血细胞预测的预测

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科学领域:

  • 生物医学工程 生物医学工程
  • 频谱学是一种光谱学.
  • 血液学 血液学 血液学

背景情况:

  • 准确的白细胞 (WBC) 计数对于临床决策至关重要.
  • 现有的光谱方法面临信号保真性和混因素的挑战.
  • 血液中的光谱采集通过减少表面工件来提高信号完整性.

研究的目的:

  • 为增强白细胞计数估计开发一个优化的光谱框架.
  • 将元启发性特征选择与生物知情子模型相结合.
  • 为了最大限度地减少血红蛋白和血小板变化的光谱干扰,以改善WBC预测.

主要方法:

  • 通过光纤探测器利用来自468名患者的高维光谱数据.
  • 使用遗传算法 (GA),火虫算法 (FA) 和灰狼优化 (GWO) 来进行特征选择.
  • 开发了基于M+N理论的生物知情子模型,将数据按血红蛋白和血小板水平进行分区.
  • 在每个子模型中训练随机森林回归模型.

主要成果:

  • 使用随机森林的火算法在低HG/高PLT子模型中实现了最高的R2 (0.864) 和最低的MAE (0.694).
  • 在特定的子模型中,GWO和GA也表现出强的表现 (分别R2=0.809和R2=0.889).
  • 特定于子模型的建模显著超过了全球回归 (R2=0.73,RMSE=1.46),提高了预测精度.

结论:

  • 生物引导的分区和元启发式优化有效地增强了光谱WBC诊断.
  • 拟议的框架提供了一个实用和准确的替代方案,用于点的护理血液学分析.
  • 这种方法表明了快速可靠的临床决策支持的潜力.