Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
Date
2020Author
Gao, Yue
Cai, Guang-Yao
Fang, Wei
Li, Hua-Yi
Wang, Si-Yuan
Chen, Lingxi
Yu, Yang
Liu, Dan
Xu, Sen
Cui, Peng-Fei
Zeng, Shao-Qing
Feng, Xin-Xia
Yu, Rui-Di
Wang, Ya
Yuan, Yuan
Jiao, Xiao-Fei
Chi, Jian-Hua
Liu, Jia-Hao
Li, Ru-Yuan
Zheng, Xu
Song, Chun-Yan
Jin, Ning
Gong, Wen-Jian
Liu, Xing-Yu
Huang, Lei
Tian, Xun
Li, Lin
Xing, Hui
Ma, Ding
Li, Chun-Rui
Ye, Fei
Gao, Qing-Lei
Metadata
Show full item recordAbstract
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.
Palabras clave
Machine learning; Early warning system; Mortality risk; COVID-19; PredictionLink to resource
https://doi.org/10.1038/s41467-020-18684-2Collections
Estadísticas Google Analytics
Comments
Respuesta Comentario Repositorio Expeditio
Gracias por tomarse el tiempo para darnos su opinión.