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

<p dir="ltr">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 pati...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Tawsifur Rahman (14150523) (author)
مؤلفون آخرون:	Muhammad E. H. Chowdhury (14150526) (author), Amith Khandakar (14151981) (author), Zaid Bin Mahbub (16869975) (author), Md Sakib Abrar Hossain (17773173) (author), Abraham Alhatou (17773176) (author), Eynas Abdalla (17773179) (author), Sreekumar Muthiyal (17773182) (author), Khandaker Farzana Islam (17773185) (author), Saad Bin Abul Kashem (17773188) (author), Muhammad Salman Khan (7202543) (author), Susu M. Zughaier (14151987) (author), Maqsud Hossain (10675896) (author)
منشور في:	2023
الموضوعات:	Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Multimodal system COVID-19 Clinical data Chest X-ray Prognostic model Deep learning Classical machine learning
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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

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