Sistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSPVALERIANO, Maria GabrielaV, Carlos R. KifferHIGINO, GianeZANAO, PalomaBARBOSA, Dulce A.MOREIRA, Patricia A.SANTOS, Paulo Caleb J. L.GRINBAUM, RenatoLORENAT, Ana Carolina2022-11-252022-11-2520222022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022), p.948-951, 2022978-1-6654-9956-9https://observatorio.fm.usp.br/handle/OPI/50290With the spread of different COVID-19 variants in the Brazilian territory, the national health system has been facing a constant overload. Using data from five different health centers located in the Sao Paulo metropolitan area, this work seeks to identify key common factors associated with the prognosis of COVID-19 severity. The proxies for severity considered are hospitalization time, death and use of mechanical ventilation. The induced models predicted objective short-term COVID-19 clinical deterioration outcomes with AUC, sensitivity and specificity up to 0.880, 0.824 and 0.833, respectively. Parameters such as C-reactive protein and percentage of neutrophils have shown most influence on the predictions. Given the nature of the lab tests highlighted, we note that innate inflammatory status in admission can play a significant role in patient outcome.engrestrictedAccessCOVID-19 prognosisMachine Learningmulticentric studyconferenceObjectCopyright IEEE COMPUTER SOC10.1109/CCGrid54584.2022.00115Computer Science, Hardware & ArchitectureComputer Science, Theory & Methods