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Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

 Chansik An  ;  Hyunsun Lim  ;  Dong-Wook Kim  ;  Jung Hyun Chang  ;  Yoon Jung Choi  ;  Seong Woo Kim 
 SCIENTIFIC REPORTS, Vol.10(1) : 18716, 2020-10 
Journal Title
Issue Date
Adult ; Aged ; Aged, 80 and over ; Coronavirus Infections / mortality* ; Female ; Humans ; Machine Learning* ; Male ; Middle Aged ; Models, Statistical ; Mortality / trends ; Pandemics ; Pneumonia, Viral / mortality* ; Republic of Korea
The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Ahn, Joong Bae(안중배) ORCID logo https://orcid.org/0000-0001-6787-1503
Chon, Hong Jae(전홍재)
Choi, Yoon Jung(최윤정) ORCID logo https://orcid.org/0000-0002-5701-8864
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