An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning
<p>COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predi...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513567182553088 |
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| author | Muhammad E. H. Chowdhury (14150526) |
| author2 | Tawsifur Rahman (14150523) Amith Khandakar (14151981) Somaya Al-Madeed (14151984) Susu M. Zughaier (14151987) Suhail A. R. Doi (7490777) Hanadi Hassen (14151990) Mohammad T. Islam (2391568) |
| author2_role | author author author author author author author |
| author_facet | Muhammad E. H. Chowdhury (14150526) Tawsifur Rahman (14150523) Amith Khandakar (14151981) Somaya Al-Madeed (14151984) Susu M. Zughaier (14151987) Suhail A. R. Doi (7490777) Hanadi Hassen (14151990) Mohammad T. Islam (2391568) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad E. H. Chowdhury (14150526) Tawsifur Rahman (14150523) Amith Khandakar (14151981) Somaya Al-Madeed (14151984) Susu M. Zughaier (14151987) Suhail A. R. Doi (7490777) Hanadi Hassen (14151990) Mohammad T. Islam (2391568) |
| dc.date.none.fl_str_mv | 2022-11-22T21:15:13Z |
| dc.identifier.none.fl_str_mv | 10.1007/s12559-020-09812-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Early_Warning_Tool_for_Predicting_Mortality_Risk_of_COVID-19_Patients_Using_Machine_Learning/21597702 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Applied computing Biological psychology Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
| dc.title.none.fl_str_mv | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)—acquired at hospital admission—were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5–50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.</p><h2>Other Information</h2> <p> Published in: Cognitive Computation<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="http://dx.doi.org/10.1007/s12559-020-09812-7" target="_blank">http://dx.doi.org/10.1007/s12559-020-09812-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_47f75ebc98d2e01336227b0f790d3da5 |
| identifier_str_mv | 10.1007/s12559-020-09812-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597702 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine LearningMuhammad E. H. Chowdhury (14150526)Tawsifur Rahman (14150523)Amith Khandakar (14151981)Somaya Al-Madeed (14151984)Susu M. Zughaier (14151987)Suhail A. R. Doi (7490777)Hanadi Hassen (14151990)Mohammad T. Islam (2391568)Applied computingBiological psychologyCognitive NeuroscienceComputer Science ApplicationsComputer Vision and Pattern Recognition<p>COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)—acquired at hospital admission—were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5–50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.</p><h2>Other Information</h2> <p> Published in: Cognitive Computation<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="http://dx.doi.org/10.1007/s12559-020-09812-7" target="_blank">http://dx.doi.org/10.1007/s12559-020-09812-7</a></p>2022-11-22T21:15:13ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s12559-020-09812-7https://figshare.com/articles/journal_contribution/An_Early_Warning_Tool_for_Predicting_Mortality_Risk_of_COVID-19_Patients_Using_Machine_Learning/21597702CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215977022022-11-22T21:15:13Z |
| spellingShingle | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning Muhammad E. H. Chowdhury (14150526) Applied computing Biological psychology Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
| status_str | publishedVersion |
| title | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| title_full | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| title_fullStr | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| title_full_unstemmed | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| title_short | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| title_sort | An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning |
| topic | Applied computing Biological psychology Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |