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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , , , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513530942717952 |
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| author | Tawsifur Rahman (14150523) |
| author2 | Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Md Sakib Abrar Hossain (17773173) Abraham Alhatou (17773176) Eynas Abdalla (17773179) Sreekumar Muthiyal (17773182) Khandaker Farzana Islam (17773185) Saad Bin Abul Kashem (17773188) Muhammad Salman Khan (7202543) Susu M. Zughaier (14151987) Maqsud Hossain (10675896) |
| author2_role | author author author author author author author author author author author author |
| author_facet | Tawsifur Rahman (14150523) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Md Sakib Abrar Hossain (17773173) Abraham Alhatou (17773176) Eynas Abdalla (17773179) Sreekumar Muthiyal (17773182) Khandaker Farzana Islam (17773185) Saad Bin Abul Kashem (17773188) Muhammad Salman Khan (7202543) Susu M. Zughaier (14151987) Maqsud Hossain (10675896) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tawsifur Rahman (14150523) Muhammad E. H. Chowdhury (14150526) Amith Khandakar (14151981) Zaid Bin Mahbub (16869975) Md Sakib Abrar Hossain (17773173) Abraham Alhatou (17773176) Eynas Abdalla (17773179) Sreekumar Muthiyal (17773182) Khandaker Farzana Islam (17773185) Saad Bin Abul Kashem (17773188) Muhammad Salman Khan (7202543) Susu M. Zughaier (14151987) Maqsud Hossain (10675896) |
| dc.date.none.fl_str_mv | 2023-05-04T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-023-08606-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/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/24980817 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | 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 |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <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 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-023-08606-w" target="_blank">https://dx.doi.org/10.1007/s00521-023-08606-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e333bcf9c047de3baf68ef2a04061f79 |
| identifier_str_mv | 10.1007/s00521-023-08606-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24980817 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical dataTawsifur Rahman (14150523)Muhammad E. H. Chowdhury (14150526)Amith Khandakar (14151981)Zaid Bin Mahbub (16869975)Md Sakib Abrar Hossain (17773173)Abraham Alhatou (17773176)Eynas Abdalla (17773179)Sreekumar Muthiyal (17773182)Khandaker Farzana Islam (17773185)Saad Bin Abul Kashem (17773188)Muhammad Salman Khan (7202543)Susu M. Zughaier (14151987)Maqsud Hossain (10675896)EngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningMultimodal systemCOVID-19Clinical dataChest X-rayPrognostic modelDeep learningClassical machine learning<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 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-023-08606-w" target="_blank">https://dx.doi.org/10.1007/s00521-023-08606-w</a></p>2023-05-04T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-023-08606-whttps://figshare.com/articles/journal_contribution/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/24980817CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249808172023-05-04T03:00:00Z |
| spellingShingle | 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 Tawsifur Rahman (14150523) 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 |
| status_str | publishedVersion |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | 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 |