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Necrosis is a form of irreversible cell death caused by severe injury such as ischemia, toxins, or trauma. Unlike programmed cell death, it is an uncontrolled, pathological process that typically provokes inflammation in surrounding tissues.Pathophysiologic ChangesNecrosis begins when cells sustain critical damage, leading to swelling of organelles, particularly mitochondria, and rapid ATP depletion. As energy levels decline, membrane ion pumps fail, leading to calcium influx and eventually,...
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A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections
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使用基于机器学习算法的传染性流体分析和临床参数来识别死软组织感染.

Chia-Peng Chang1,2, Chung-Jen Lin1, Wen-Chih Fann1

  • 1Department of Emergency Medicine, Chang Gung Memorial Hospital, No. 6, W. Sec., Jiapu Rd., Puzih City, Chiayi County, 613, Taiwan.

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概括

人工智能 (AI) 机器学习模型可以有效地诊断死软组织感染 (NSTI). 与其他算法和液体乳酸盐水平相比,随机森林模型显示出更高的准确性.

关键词:
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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习
  • 传染病诊断 传染病诊断 传染病诊断

背景情况:

  • 结核性软组织感染 (NSTI) 的诊断是具有挑战性的.
  • 人工智能 (AI) 和机器学习 (ML) 提供高效,精确的疾病识别.
  • 在诊断中AI/ML的采用正在增加.

研究的目的:

  • 开发和评估用于NSTI诊断的ML模型.
  • 为了比较不同ML算法的诊断性能.
  • 为了确定一个优化的NSTI诊断模型的关键特征.

主要方法:

  • 训练了四个ML模型 (随机森林,KNN,SVM,后勤回归) 在13名NSTI和12名细胞炎患者的数据上.
  • 利用了通过统计分析识别的28个不同的特征.
  • 开发了一种精细的随机森林模型,使用6个最有影响力的特征.

主要成果:

  • 随机森林模型实现了89.6%的灵敏度和92.9%的特异性.
  • 优化的随机森林模型显示了90.2%的灵敏度和92.2%的特异性.
  • 随机森林在诊断准确性方面表现优于其他ML模型和液体乳酸盐水平.

结论:

  • 一个基于森林的随机ML模型为NSTI提供了一种高效,低成本,快速的诊断工具.
  • 这种人工智能驱动的方法可以增强NSTI管理的临床决策.
  • 开发的模型为医疗保健从业者提供了在识别和治疗NSTI方面更高的有效性.