Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.
Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.
复制标题
基于机器学习的早期预警系统能够准确预测 COVID-19 的死亡风险
DOI：
10.1038/s41467-020-18684-2
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发表时间：
2020-10-06
影响因子：
16.6
通讯作者：
Gao QL
中科院分区：
文献类型：
--
作者：
Gao Y;Cai GY;Fang W;Li HY;Wang SY;Chen L;Yu Y;Liu D;Xu S;Cui PF;Zeng SQ;Feng XX;Yu RD;Wang Y;Yuan Y;Jiao XF;Chi JH;Liu JH;Li RY;Zheng X;Song CY;Jin N;Gong WJ;Liu XY;Huang L;Tian X;Li L;Xing H;Ma D;Li CR;Ye F;Gao QL
冠状病毒病（COVID-19）病例飙升正在打击全球卫生系统。不堪重负的卫生设施已成为缓解疫情的必要条件，但COVID-19的死亡率继续上升。在这里，我们提出了一个针对COVID-19的死亡风险预测模型（MRPMC），该模型使用患者入院时的临床数据，按死亡风险对患者进行分层，从而能够提前20天预测生理恶化和死亡。该集成模型使用四种机器学习方法构建，包括Logistic回归，支持向量机，梯度提升决策树和神经网络。我们在一个内部验证队列和两个外部验证队列中验证了MRPMC，其AUC分别为0.9621（95% CI：0.9464-0.9778）、0.9760（0.9613-0.9906）和0.9246（0.8763-0.9729）。该模型能够对COVID-19患者进行快速准确的死亡风险分层，并可能促进有利于高风险COVID-19患者的更具响应性的卫生系统。
Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.
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