Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables
Abstract Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52529-y |
| _version_ | 1797276617842622464 |
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| author | Hamid Reza Niazkar Jalil Moshari Abdoljavad Khajavi Mohammad Ghorbani Majid Niazkar Aida Negari |
| author_facet | Hamid Reza Niazkar Jalil Moshari Abdoljavad Khajavi Mohammad Ghorbani Majid Niazkar Aida Negari |
| author_sort | Hamid Reza Niazkar |
| collection | DOAJ |
| description | Abstract Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended. |
| first_indexed | 2024-03-07T15:30:48Z |
| format | Article |
| id | doaj.art-8450328663da4f208f321703a3b71d56 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:30:48Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8450328663da4f208f321703a3b71d562024-03-05T16:25:18ZengNature PortfolioScientific Reports2045-23222024-01-0114111110.1038/s41598-024-52529-yApplication of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variablesHamid Reza Niazkar0Jalil Moshari1Abdoljavad Khajavi2Mohammad Ghorbani3Majid Niazkar4Aida Negari5Gonabad University of Medical SciencesPediatric Department, School of Medicine, Gonabad University of Medical SciencesCommunity Medicine Department, School of Medicine, Gonabad University of Medical SciencesLaboratory hematology and Transfusion medicine, Department of Medical Laboratory Sciences, Faculty of Allied Medicine, Gonabad University of Medical SciencesFaculty of Engineering, Free University of Bozen-Bolzano, Piazza Università 5Gonabad University of Medical SciencesAbstract Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.https://doi.org/10.1038/s41598-024-52529-y |
| spellingShingle | Hamid Reza Niazkar Jalil Moshari Abdoljavad Khajavi Mohammad Ghorbani Majid Niazkar Aida Negari Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables Scientific Reports |
| title | Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables |
| title_full | Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables |
| title_fullStr | Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables |
| title_full_unstemmed | Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables |
| title_short | Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables |
| title_sort | application of multi gene genetic programming to the prognosis prediction of covid 19 using routine hematological variables |
| url | https://doi.org/10.1038/s41598-024-52529-y |
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