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In Intelligence-based medicine

Background : Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.

Method : The Cerner Real-World Database was used for this study. Data on adult patients (18 years or older) with cardiovascular and related circulatory diseases between 2017 and 2019 were retrieved and a total of 13 these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a single more powerful super learning model for predicting COVID-19 severity on admission to the hospital.

Result : LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).

Conclusion : Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Ehwerhemuepha Louis, Danioko Sidy, Verma Shiva, Marano Rachel, Feaster William, Taraman Sharief, Moreno Tatiana, Zheng Jianwei, Yaghmaei Ehsan, Chang Anthony


COVID-19, COVID-19 severity, Super learning, cardiovascular conditions, ensemble learning, predicting COVID-19 severity