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. 2021 Apr;13(4):2569-2574.
doi: 10.21037/jtd-20-2934.

COVID-19 epidemic in a Respiratory Diseases Unit: predictor ranking and mining

Giulia M Stella  1 , Davide Piloni  1 , Giulia Accordino  1 , Amelia Grosso  1 , Federica Albicini  1 , Erica Gini  1 , Silvia Mancinelli  1 , Matteo Della Zoppa  1 , Andrea P G Marchelli  2 , Chandra Bortolotto  3 , Angelo G Corsico  1
Affiliations

COVID-19 epidemic in a Respiratory Diseases Unit: predictor ranking and mining

Giulia M Stella et al. J Thorac Dis. 2021 Apr.
No abstract available

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jtd-20-2934). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Patient characteristic and data mining analysis. (A) Demographic and clinical features of the cohort evaluated. (B) Partition analysis in male and female patients. Lower values indicate better fit. O2, oxygen; TEP, pulmonary thrombo-embolism; CPE, cardio-pulmonary oedema; CRP, C reactive protein; Count, number of training observation; G2, Gini index.
Figure 2
Figure 2
Main predictors with their value and rank. Predictors with higher contribution are likely to impact on patient outcome. (A) Partition analysis applied to predictor ranking in females; (B) Partition analysis applied to predictor ranking in males.
See this image and copyright information in PMC

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