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10.1038/s41598-020-75767-2

http://scihub22266oqcxt.onion/10.1038/s41598-020-75767-2
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suck abstract from ncbi


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pmid33127965      Sci+Rep 2020 ; 10 (1): 18716
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  • Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study #MMPMID33127965
  • An C; Lim H; Kim DW; Chang JH; Choi YJ; Kim SW
  • Sci Rep 2020[Oct]; 10 (1): 18716 PMID33127965show ga
  • 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 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.
  • |*Machine Learning[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |COVID-19[MESH]
  • |Coronavirus Infections/*mortality[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Models, Statistical[MESH]
  • |Mortality/trends[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*mortality[MESH]


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