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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 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 is a Computer Science article available to read on EtoBox.

What is 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 about?

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 __F__1-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 (O~2~%), White Blood Cells Count, C-r

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Author

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

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)