Unveiling the Hidden Burden of COVID-19 in Brazil's obstetric population with Severe Acute Respiratory Syndrome: a machine learning model
This cross-sectional study analyzed retrospective data of pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021. Patients were divided into two groups (COVID-19 and non-COVID-19) for comparative analysis, and a XGBoost predictive model was constructed to classify cases without a defined causative agent. The results suggest that the number of COVID-19 cases and deaths in the obstetric population was much higher than documented by authorities, indicating a significant impact on the maternal mortality ratio during this period.
Funding: Bill & Melinda Gates Foundation, CNPq and FAPES.
SINASC - 01.data/sinasc/
SIVEP-Gripe - 01.data/sivep-gripe/
Data processing, descriptive analysis, modeling and interpretability - 02.script/
Tables - 03.results/tabs/
Figures - 03.results/figs/
Supplementary material - 03.results/supplementary/
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