Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings
Hooman H. Rashidi(University of California, Davis), Imran Khan(University of California, Davis), Amna Ali(De Montfort University), Adnan Bashir(National Institute of Health), Naeem Akhter(Rawalpindi Medical University), Tanzeel Zohra(National Institute of Health), Brandon D. Fennell(University of California, San Francisco), Luke T. Dang(University of California, Davis), Resmi Ravindran(University of California, Davis), Mohammed Umer(Rawalpindi Medical University), R. Hussain Butt(Shifa International Hospital), Rana I. Sikandar(Pakistan Institute of Medical Sciences), Mohammad Mudassar(National Institute of Health), Aamer Ikram(Ministry of National Health Services Regulation and Coordination), Hamza Tanvir(National Institute of Health), Nasim Akhtar(Pakistan Institute of Medical Sciences)
Cited by 11
Related Papers
Electrohydrodynamic atomisation driven design and engineering of opportunistic particulate systems for applications in drug delivery, therapeutics and pharmaceutics
|Advanced Drug Delivery Reviews|2021|64
The global case fatality rate of coronavirus disease 2019 by continents and national income: A meta‐analysis
|Journal of Medical Virology|2022|60
A comprehensive review on clinical and mechanistic pathophysiological aspects of COVID-19 Malady: How far have we come?
|Virology Journal|2021|34
Immune response dynamics in COVID-19 patients to SARS-CoV-2 and other human coronaviruses
|PLoS ONE|2021|29
Antioxidant activity, acetylcholinesterase inhibitory potential and phytochemical analysis of Sarcocephalus latifolius Sm. bark used in traditional medicine in Sudan
|BMC Complementary and Alternative Medicine|2017|27