Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records.
BMC Med Inform Decis Mak
; 22(1): 187, 2022 07 17.
Article
in English
| MEDLINE | ID: covidwho-1938312
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
BMC Med Inform Decis Mak
Journal subject:
Medical Informatics
Year:
2022
Document Type:
Article
Affiliation country:
S12911-022-01931-5
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