多维动态预测模型用于医院住院的患者与中国的Omicron变种
Yujie Chen1, Yao Wang2, Jieqing Chen3
1Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Infectious Disease Modelling
|October 19, 2023
概括
机器学习模型使用日常数据准确预测COVID-19患者的恶化和恢复. 关键预测因素包括并发症,年龄,病程以及改善患者管理的生命体征.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 传染病建模 传染病建模
背景情况:
- 由于COVID-19的流行,特别是Omicron变种,需要对患者进行准确的预后.
- 动态预测患者状况对于有效的资源配置和治疗策略至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测住院COVID-19患者的临床过程.
- 利用日常的多维数据进行动态的,短期的预后.
主要方法:
- XGBoost模型是根据995名住院COVID-19患者的每日数据进行训练的.
- 数据包括人口统计,并发症,实验室结果,生命体征和治疗方法.
- 沙普利添加式解释 (SHAP) 用于特征重要性分析.
主要成果:
- 恶化预测实现了接收器运行特征曲线 (AUROC) 下面的面积为0.872的第二天和0.786的7天.
- 恢复预测实现了第二天的AUROC为0.823,3天的AUROC为0.675.
- 病情恶化的关键预测因素包括病程,住院时间和高血压;对于康复,他们包括年龄,D-二次数和皮质类固醇治疗.
结论:
- 机器学习模型可以有效地使用每日变量预测COVID-19患者状况动态.
- 重要的预测特征包括并发症 (例如,高脂血症),年龄,病程,生命体征,D-二次体和氧疗法.
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