Survival prediction of COVID-19 patients using multi-modal dataset

ABSTRACT

COVID-19 has proved to be a global emergency that has fractured the healthcare systems to the extent that its impact is too challenging to encompass. Though many Computer-Aided Diagnoses (CAD) systems have been developed for automatic detection of COVID-19 from Chest X-rays and chest CT images, very few works have been done on detecting COVID-19 from a clinical dataset. Resources needed for obtaining Clinical data like blood pressure, liver disease, past traveling history, etc., are inexpensive compared to collecting Chest CT images for COVID-19 infected patients. We propose a novel multi-model dataset for the survival prediction of patients infected with COVID-19. The dataset proposed is collected and created at Mahatma Gandhi Memorial Medical College, Indore. The dataset contains clinical data and chest X-ray images obtained from the same patient infected with COVID-19. For proper prognosis of the COVID19 positive patients from the clinical dataset, we have proposed a Bi-Stream Gated Attention-based CNN (BSGA-CNN) model. The BSGA-CNN model achieved an accuracy of 96.90% (± 3.05%). A CNN based on pre-trained VGG-Net is used to classify the corresponding Chest X-Ray images. It gave an accuracy of 87.76% (± 8.78%)%. © 2022 IEEE.