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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

journal contribution
submitted on 2024-01-11, 09:09 and posted on 2024-01-15, 07:55 authored by 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

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.

Other Information

Published in: Neural Computing and Applications
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1007/s00521-023-08606-w

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • Qatar University Health - QU
  • College of Medicine - QU HEALTH
  • Hamad Medical Corporation
  • Hamad General Hospital - HMC
  • AFG College with the University of Aberdeen