Finding of the factors affecting the severity of COVID-19 based on mathematical models
Abstract Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-03632-x |
| _version_ | 1819102718436835328 |
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| author | Jiahao Qu Brian Sumali Ho Lee Hideki Terai Makoto Ishii Koichi Fukunaga Yasue Mitsukura Toshihiko Nishimura |
| author_facet | Jiahao Qu Brian Sumali Ho Lee Hideki Terai Makoto Ishii Koichi Fukunaga Yasue Mitsukura Toshihiko Nishimura |
| author_sort | Jiahao Qu |
| collection | DOAJ |
| description | Abstract Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity. |
| first_indexed | 2024-12-22T01:39:01Z |
| format | Article |
| id | doaj.art-35b23ef86bfc48fca7f440dde3a07ca5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-22T01:39:01Z |
| publishDate | 2021-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-35b23ef86bfc48fca7f440dde3a07ca52022-12-21T18:43:17ZengNature PortfolioScientific Reports2045-23222021-12-011111710.1038/s41598-021-03632-xFinding of the factors affecting the severity of COVID-19 based on mathematical modelsJiahao Qu0Brian Sumali1Ho Lee2Hideki Terai3Makoto Ishii4Koichi Fukunaga5Yasue Mitsukura6Toshihiko Nishimura7School of Integrated Design Engineering, Keio UniversitySchool of Integrated Design Engineering, Keio UniversityDepartment of Neuropsychiatry, Keio University School of MedicineDepartment of Neuropsychiatry, Keio University School of MedicineDepartment of Neuropsychiatry, Keio University School of MedicineDepartment of Neuropsychiatry, Keio University School of MedicineSchool of Integrated Design Engineering, Keio UniversityDepartment of Anesthesia, Stanford University School of MedicineAbstract Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.https://doi.org/10.1038/s41598-021-03632-x |
| spellingShingle | Jiahao Qu Brian Sumali Ho Lee Hideki Terai Makoto Ishii Koichi Fukunaga Yasue Mitsukura Toshihiko Nishimura Finding of the factors affecting the severity of COVID-19 based on mathematical models Scientific Reports |
| title | Finding of the factors affecting the severity of COVID-19 based on mathematical models |
| title_full | Finding of the factors affecting the severity of COVID-19 based on mathematical models |
| title_fullStr | Finding of the factors affecting the severity of COVID-19 based on mathematical models |
| title_full_unstemmed | Finding of the factors affecting the severity of COVID-19 based on mathematical models |
| title_short | Finding of the factors affecting the severity of COVID-19 based on mathematical models |
| title_sort | finding of the factors affecting the severity of covid 19 based on mathematical models |
| url | https://doi.org/10.1038/s41598-021-03632-x |
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