BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Tawsifur Rahman; Muhammad E H Chowdhury; Amith Khandakar; Zaid Bin Mahbub; Md Sakib Abrar Hossain; Abraham Alhatou; Eynas Abdalla; Sreekumar Muthiyal; Khandaker Farzana Islam; Saad Bin Abul Kashem; Muhammad Salman Khan; Susu M Zughaier; Maqsud Hossain

doi:10.1007/s00521-023-08606-w

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Neural Comput Appl. 2023 May 4:1-23. doi: 10.1007/s00521-023-08606-w. Online ahead of print.

Affiliations

¹ Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar.
² Department of Physics and Mathematics, North South University, Dhaka, 1229 Bangladesh.
³ NSU Genome Research Institute (NGRI), North South University, Dhaka, 1229 Bangladesh.
⁴ Department of Biology, University of South Carolina (USC), Columbia, SC 29208 USA.
⁵ Anesthesia Department, Hamad General Hospital, P.O. Box 3050, Doha, Qatar.
⁶ Department of Radiology, Hamad General Hospital, P.O. Box 3050, Doha, Qatar.
⁷ Department of Computer Science, AFG College with the University of Aberdeen, Doha, Qatar.
⁸ Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar.

Abstract

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O₂%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Keywords: COVID-19; Chest X-ray; Classical machine learning; Clinical data; Deep learning; Multimodal system; Prognostic model.