1. A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients
- Author
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Pedro Ventura-Aguiar, Beatriu Bayés, Josep M. Campistol, Vicens Torregrosa, Antonio Alcaraz, Francisco J. Santos-Arteaga, Jessica Ugalde-Altamirano, Enrique Montagud-Marrahi, David Cucchiari, Gastón J Piñeiro, Nuria Esforzado, Federico Oppenheimer, Fritz Diekmann, Esteban Poch, Asunción Moreno, Ignacio Revuelta, Marta Bodro, Frederic Cofan, and Debora Di Caprio
- Subjects
Artificial neural network ,Linguistics and Language ,Computer science ,Logistic regression ,030230 surgery ,Machine learning ,computer.software_genre ,Article ,Language and Linguistics ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Data envelopment analysis ,030212 general & internal medicine ,Kidney transplant ,Cluster analysis ,Envelopment ,COVID-19 ,Random forest ,business.industry ,Identification (information) ,Cohort ,Artificial intelligence ,business ,computer - 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. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10008-0.
- Published
- 2021
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