| Autor: |
Mahdi Mahdavi, Hadi Choubdar, Erfan Zabeh, Michael Rieder, Safieddin Safavi-Naeini, Vida Khanlarzadeh, Zsolt Jobbagy, Amirata Ghorbani, Atefeh Abedini, Arda Kiani, Reza Lashgari, Ehsan Kamrani |
| Rok vydání: |
2020 |
| Popis: |
With no effective treatment currently available and maximum preventive measures already in place, more interventions in the clinical field are needed to decrease COVID-19 patient mortality. Early prediction of mortality risk in COVID-19 patients can decrease mortality by assuring efficient resource allocation and treatment planning. This study conducts an early prediction of COVID-19 prognosis using laboratory, clinical, and demographic data collected from patients in the first day of admission. Three machine learning models were developed to investigate and compare the prediction power of invasive and noninvasive biomarkers. The results suggest that early mortality prediction of patients via non-invasive biomarkers provides significant accuracy and can be used as a triage assisting tool without the need for additional costs or waiting time of laboratory tests. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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