Comparing Machine Learning Screening Approaches Using Clinical Data and Cytokine Profiles for COVID-19 in Resource-Limited and Resource-Abundant Settings.

Hooman H. Rashidi,Aamer Ikram,Luke T. Dang,Adnan Bashir,Tanzeel Zohra,Amna Ali, Hamza Tanvir, Mohammad Mudassar,Resmi Ravindran,Nasim Akhtar, Rana I. Sikandar, Mohammed Umer, Naeem Akhter, Rafi Butt, Brandon D. Fennell,Imran H. Khan

Scientific Reports（2024）

Cited 1|Views10

Key words

COVID-19,Cytokines,Chemokines,Binary classifier,Machine learning,FGF basic (FGF2),Eotaxin (CCL11),G-CSF (CSF3),GM-CSF (CSF2),IFN-gamma (IFNG),IL-1 beta (IL1B),IL-1ra (IL1RN),IL-1 alpha (IL1A),IL-2R alpha (IL2RA),IL3,IL-12 (p40) (IL12B),IL16,IL2,IL4,IL5,IL6,IL7,IL8 (CXCL8),IL9,GRO-alpha (CXCL1),HGF,IFN-alpha 2 (IFNA2),LIF,MCP-3 (CCL7),IL10,IL-12 (p70) (IL12A),IL13,IL15,IL17A,IP-10 (CXCL10),MCP-1 (MCAF) (CCL2),MIG (CXCL9),beta-NGF (NGF),SCF (KITLG),SCGF-beta (CLEC11A),SDF-1 alpha (CXCL12),MIP-1 alpha (CCL3),MIP-1 beta (CCL4),PDGF-BB (PDGFB),RANTES (CCL5),TNF-alpha,VEGF (VEGFA),CTACK (CCL27),MIF,TRAIL (TNFSF10),IL-18 (IL18),M-CSF (CSF1),TNF-beta (LTA)

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