Let the data speak: analysing data from multiple health centers of the Sao Paulo metropolitan area for COVID-19 clinical deterioration prediction

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Citações na Scopus
2
Tipo de produção
conferenceObject
Data de publicação
2022
DOI
Scopus
Web of Science
Título da Revista
ISSN da Revista
Título do Volume
Editora
IEEE COMPUTER SOC
Autores
VALERIANO, Maria Gabriela
V, Carlos R. Kiffer
HIGINO, Giane
ZANAO, Paloma
BARBOSA, Dulce A.
GRINBAUM, Renato
LORENAT, Ana Carolina
Citação
2022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022), p.948-951, 2022
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
With 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.
Palavras-chave
COVID-19 prognosis, Machine Learning, multicentric study
URI
https://observatorio.fm.usp.br/handle/OPI/50290
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Coleções
Comunicações em Eventos - FM/Outros
Comunicações em Eventos - COVID-19
Comunicações em Eventos - LIM/13
Comunicações em Eventos - ODS/03