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المؤلفRahman, Tawsifur
المؤلفChowdhury, Muhammad E. H.
المؤلفKhandakar, Amith
المؤلفMahbub, Zaid Bin
المؤلفHossain, Md Sakib Abrar
المؤلفAlhatou, Abraham
المؤلفAbdalla, Eynas
المؤلفMuthiyal, Sreekumar
المؤلفIslam, Khandaker Farzana
المؤلفKashem, Saad Bin Abul
المؤلفKhan, Muhammad Salman
المؤلفZughaier, Susu M.
المؤلفHossain, Maqsud
تاريخ الإتاحة2023-10-24T08:17:13Z
تاريخ النشر2023
اسم المنشورNeural Computing and Applications
المصدرScopus
الرقم المعياري الدولي للكتاب9410643
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s00521-023-08606-w
معرّف المصادر الموحدhttp://hdl.handle.net/10576/48802
الملخصNowadays, 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.
راعي المشروعOpen Access funding provided by the Qatar National Library. This work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعChest X-ray
Classical machine learning
Clinical data
COVID-19
Deep learning
Multimodal system
Prognostic model
العنوانBIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
النوعArticle
الصفحات17461-17483
رقم العدد24
رقم المجلد35
dc.accessType Abstract Only


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