The pandemic of coronavirus disease 2019 (COVID-19) has severely impacted the world. Several studies suggest an increased risk for COVID-19 patients with underlying cardiovascular diseases (CVD). However, it is challenging to quantify such risk factors and integrate them into patient condition evaluation. This paper presents machine learning methods to assess CVD risk scores from chest computed tomography together with laboratory data, demographics, and deep learning extracted lung imaging features to increase the outcome prediction accuracy for COVID-19 patients. The experimental results demonstrate an overall increase in prediction performance when the CVD severity score was added to the feature set. The machine learning methods obtained their best performance when all categories of the features were used for the patient outcome prediction. With the best attained area under the curve of 0.888, the presented research may assist physicians in clinical decision-making process on managing COVID-19 patients.
Machine Learning in Medical Imaging 2021. Lecture Notes in Computer Science, Vol 12966. Springer, Cham. (2021)