重建细胞因子视图用于COVID-19死亡率的多视图预测
Yueying Wang1,2,3,4, Zhao Wang5, Yaqing Liu1
1College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
BMC infectious diseases
|September 22, 2023
概括
这项研究开发了一种使用完整血细胞计数来预测细胞因子水平的COVID-19死亡率预测模型. 与原始值相比,预测的细胞因子水平显著提高了死亡率预测的准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 新型冠状病毒病2019 (COVID-19) 构成重大威胁,需要准确的死亡率预测,以便为患者提供护理和资源配置.
- 在COVID-19感染期间,全血细胞计数 (CBC) 和细胞因子水平发生变化.
- 与细胞因子水平不同的是,CBCs很容易获得,这凸显了需要可访问的预测标志物.
研究的目的:
- 开发一个准确的COVID-19死亡率预测模型,使用随时可用的全血计数.
- 探索从CBC数据预测细胞因子水平的可行性.
- 通过将预测的细胞因子水平与CBC数据相结合,提高COVID-19死亡率预测.
主要方法:
- 通过自编码器,主要成分分析和线性回归利用完整的血液计数来预测细胞因子水平.
- 在死亡预测中使用支持向量机分类器和适应性增强器来选择特征.
- 开发了细胞因子水平和COVID-19患者死亡率的预测模型.
主要成果:
- 全血细胞计数实现了COVID-19死亡率分类的曲线下的区域 (AUC) 0.9678.
- 预测的细胞因子水平,仅取决于特征集,为死亡率分类产生了0.9844的优异AUC.
- 预测的细胞因子水平与COVID-19死亡率的关联比原始细胞因子测量更强.
结论:
- 将预测的细胞因子水平与CBC数据相结合,显著提高了COVID-19死亡率预测模型.
- 开发的用于预测细胞因子水平和预测COVID-19死亡率的模型是公开的.
- 这种方法提供了一种具有成本效益和可访问的方法,用于改进COVID-19死亡风险评估.
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