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
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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.
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.
期刊介绍:
Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems.
All items relevant to building practical systems are within its scope, including but not limited to:
-adaptive computing-
algorithms-
applicable neural networks theory-
applied statistics-
architectures-
artificial intelligence-
benchmarks-
case histories of innovative applications-
fuzzy logic-
genetic algorithms-
hardware implementations-
hybrid intelligent systems-
intelligent agents-
intelligent control systems-
intelligent diagnostics-
intelligent forecasting-
machine learning-
neural networks-
neuro-fuzzy systems-
pattern recognition-
performance measures-
self-learning systems-
software simulations-
supervised and unsupervised learning methods-
system engineering and integration.
Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.