Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters

<p>The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the s...

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Bibliographic Details
Main Author: Tawsifur Rahman (14150523) (author)
Other Authors: Amith Khandakar (14151981) (author), Md Enamul Hoque (5807120) (author), Nabil Ibtehaz (16888773) (author), Saad Bin Kashem (16888776) (author), Reehum Masud (16888779) (author), Lutfunnahar Shampa (16888782) (author), Mohammad Mehedi Hasan (7326065) (author), Mohammad Tariqul Islam (7854059) (author), Somaya Al-Maadeed (5178131) (author), Susu M. Zughaier (14151987) (author), Saif Badran (16888785) (author), Suhail A. R. Doi (7490777) (author), Muhammad E. H. Chowdhury (14150526) (author)
Published: 2021
Subjects:
Biomedical and clinical sciences
Cardiovascular medicine and haematology
Health sciences
Epidemiology
Health services and systems
Information and computing sciences
Machine learning
COVID-19
Hospitals
Biomarkers
Biological system modeling
Pulmonary diseases
Predictive models
Complete blood count
Prognostic model
Early prediction of mortality risk
Faculty of Robotics and Advanced Computing
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