In 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.

A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients / Revuelta, I.; Santos-Arteaga, F. J.; Montagud-Marrahi, E.; Ventura-Aguiar, P.; Di Caprio, D.; Cofan, F.; Cucchiari, D.; Torregrosa, V.; Pineiro, G. J.; Esforzado, N.; Bodro, M.; Ugalde-Altamirano, J.; Moreno, A.; Campistol, J. M.; Alcaraz, A.; Bayes, B.; Poch, E.; Oppenheimer, F.; Diekmann, F.. - In: ARTIFICIAL INTELLIGENCE REVIEW. - ISSN 0269-2821. - 54:(2021), pp. 4653-4684. [10.1007/s10462-021-10008-0]

A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients

Di Caprio D.;
2021-01-01

Abstract

In 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.
2021
Revuelta, I.; Santos-Arteaga, F. J.; Montagud-Marrahi, E.; Ventura-Aguiar, P.; Di Caprio, D.; Cofan, F.; Cucchiari, D.; Torregrosa, V.; Pineiro, G. J....espandi
A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients / Revuelta, I.; Santos-Arteaga, F. J.; Montagud-Marrahi, E.; Ventura-Aguiar, P.; Di Caprio, D.; Cofan, F.; Cucchiari, D.; Torregrosa, V.; Pineiro, G. J.; Esforzado, N.; Bodro, M.; Ugalde-Altamirano, J.; Moreno, A.; Campistol, J. M.; Alcaraz, A.; Bayes, B.; Poch, E.; Oppenheimer, F.; Diekmann, F.. - In: ARTIFICIAL INTELLIGENCE REVIEW. - ISSN 0269-2821. - 54:(2021), pp. 4653-4684. [10.1007/s10462-021-10008-0]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/307097
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