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• W4360993119 abstract "Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants.A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method.The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one.Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing." @default.
• W4360993119 created "2023-03-30" @default.
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• W4360993119 date "2023-03-01" @default.
• W4360993119 modified "2023-10-17" @default.
• W4360993119 title "Hemogram‐based decision tree models for discriminating <scp>COVID</scp> ‐19 from <scp>RSV</scp> in infants" @default.
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• W4360993119 doi "https://doi.org/10.1002/jcla.24862" @default.
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