Article ; Online: Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.
Journal of the American College of Emergency Physicians open
2020 Volume 1, Issue 6, Page(s) 1364–1373
Abstract: 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 ... ...
| Abstract | 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 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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| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-08-25 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 2688-1152 |
| ISSN (online) | 2688-1152 |
| DOI | 10.1002/emp2.12205 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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