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Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.
- Source :
-
European Journal of Internal Medicine . Jul2024, Vol. 125, p67-73. 7p. - Publication Year :
- 2024
-
Abstract
- • Determining the risk for intensive care in COVID-19 patients is essential. • Artificial neural networks may provide reliable predictions. • We used a data ensemble refinement greedy algorithm (DERGA) on data from 1596 patients. • The optimal prediction model was based on only four hematological parameters. • The best prediction corresponded to a particularly high accuracy of 97.12 %. It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09536205
- Volume :
- 125
- Database :
- Academic Search Index
- Journal :
- European Journal of Internal Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 178045773
- Full Text :
- https://doi.org/10.1016/j.ejim.2024.02.037