Datasets Created from Routine Laboratory Parameters for Use in the Diagnosis, Prognosis, and Mortality of COVID-19
| Author | Mehmet Tahir HUYUT | en |
| Orcid | Mehmet Tahir HUYUT [0000-0002-2564-991X] | en |
| Issued Date | 2023-04-30 | en |
| Abstract | It is important to diagnose coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 at an early stage and to monitor severely infected patients in order to reduce the lethality of the disease. In addition, there is a need for alternative methods with lower costs and faster results to determine the severity of the disease. In this context, routine blood values can be used to determine the diagnosis/prognosis and mortality of COVID-19. In this study, three optimized datasets were prepared to determine the features that affect the diagnosis, prognosis, and mortality of COVID-19. These datasets can be used by researchers to determine the diagnosis and severity of COVID-19 with various classifier machine learning models and artificial intelligence methods. It is hoped that studies on these datasets will reduce the negative pressures on the health system and provide important clinical guidance for decision-makers in the diagnosis and prognosis of COVID-19. | en |
| DOI | https://doi.org/10.5812/jjhs-136913 | en |
| Keyword | COVID-19 | en |
| Keyword | Diagnosis | en |
| Keyword | Prognosis | en |
| Keyword | Mortality | en |
| Keyword | Biochemical and Hematological Biomarkers | en |
| Keyword | Routine Blood Values | en |
| Keyword | Feature Selection | en |
| Keyword | Artificial Intelligence | en |
| Keyword | Machine Learning | en |
| Keyword | Neural Network | en |
| Publisher | Brieflands | en |
| Title | Datasets Created from Routine Laboratory Parameters for Use in the Diagnosis, Prognosis, and Mortality of COVID-19 | en |
| Type | Methods Article | en |
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