Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records

Autor: Davi Silva Rodrigues, Ana Catharina S. Nastri, Marcello M. Magri, Maura Salaroli de Oliveira, Ester C. Sabino, Pedro H. M. F. Figueiredo, Anna S. Levin, Maristela P. Freire, Leila S. Harima, Fátima L. S. Nunes, João Eduardo Ferreira, HCFMUSP COVID-19 Study Group
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-15 (2022)
Druh dokumentu: article
ISSN: 1472-6947
DOI: 10.1186/s12911-022-01931-5
Popis: Abstract Background COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging. Methods We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient’s outcome. Results Time series-based machine learning models are capable of predicting a COVID-19 patient’s outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction). Conclusions Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.
Databáze: Directory of Open Access Journals
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