scholarly journals Logistics efficiency in small and medium enterprises: A logistics, data envelopment analysis combined with artificial neural network (DEA-ANN) approach

2012 ◽  
Vol 6 (49) ◽  
pp. 11820-11827 ◽  
Author(s):  
R M Campos Garcia ◽  
M A Garcia Vidales ◽  
M Y Garcia Vidales ◽  
O Gonzalez Gomez ◽  
A Altamirano Corro
Author(s):  
Ignacio Revuelta ◽  
Francisco J. Santos-Arteaga ◽  
Enrique Montagud-Marrahi ◽  
Pedro Ventura-Aguiar ◽  
Debora Di Caprio ◽  
...  

AbstractIn an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)—Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model.


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