Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Nature communications ; h5-index 260.0

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.

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