预测COVID-19和肺炎患者的临床结果:一种机器学习方法
Kaida Cai1,2,3, Zhengyan Wang2, Xiaofang Yang2
1Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Viruses
|October 26, 2024
概括
对包括COVID-19在内的严重肺炎机械通风患者预测出院结果至关重要. XGBoost和随机森林归算有效地处理缺失的数据,并提高预测准确性,以便做出更好的临床决策.
科学领域:
- 临床医学 临床医学
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 准确预测机械通风重症患者,特别是COVID-19患者的出院结果,对于临床决策至关重要.
- 医学研究中缺少的数据对分析结果的有效性构成重大挑战.
- COVID-19大流行凸显了在重症监护机构需要强大的预测模型的必要性.
研究的目的:
- 开发和评估机械通风患者严重肺炎出院结果的预测模型.
- 为了比较不同缺失数据归算技术 (多重归算,missForest) 和特征选择方法 (SCAD惩罚后勤回归) 的有效性.
- 评估各种机器学习算法 (ELM,RF,SVM,XGBoost) 的性能,以预测结果.
主要方法:
- 采用多重归算和错过Forest用于缺失的数据归算,以提高数据的完整性.
- 使用的SCAD对显著特征选择的逻辑回归进行了惩罚.
- 极端学习机器 (ELM),随机森林 (RF),支持矢量机器 (SVM) 和XGBoost使用对真实世界的临床数据进行10倍交叉验证的比较预测性能.
主要成果:
- 与ELM,RF和SVM相比,XGBoost在预测放电结果方面始终表现出优异的性能.
- 随机森林归算方法总体上提高了模型性能,在管理缺失数据方面超过了多重归算.
- 使用SCAD的特征选择处罚后勤回归有助于确定释放结果的重要预测因素.
结论:
- XGBoost 是一个可靠的工具,用于预测机械通风患者严重肺炎,包括COVID-19病例的出院结果.
- 随机森林归算是处理该临床队列中缺少数据的有效策略,提高了预测准确度.
- 集成先进的归算和机器学习技术可以改善临床决策对于重症患者.
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