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10.1002/emp2.12205

http://scihub22266oqcxt.onion/10.1002/emp2.12205
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32838390!7405082!32838390
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suck abstract from ncbi


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pmid32838390      J+Am+Coll+Emerg+Physicians+Open 2020 ; 1 (6): 1364-1373
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  • Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients #MMPMID32838390
  • Zhu JS; Ge P; Jiang C; Zhang Y; Li X; Zhao Z; Zhang L; Duong TQ
  • J Am Coll Emerg Physicians Open 2020[Dec]; 1 (6): 1364-1373 PMID32838390show ga
  • OBJECTIVE: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. METHODS: This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). RESULTS: Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O(2) Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. CONCLUSIONS: Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.
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