A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients
Ignacio Revuelta, Francisco J. Santos‐Arteaga, Enrique Montagud‐Marrahí, Pedro Ventura‐Aguiar, Debora Di Caprio, Frederic Cofán, David Cucchiari, Vicens Torregrosa, Gastón Piñeiro, Núria Esforzado, Marta Bodro, Jessica Ugalde, Asunción Moreno, Josep M. Campistol, Antonio Alcaraz, Beatriu Bayés, Esteban Poch, Federico Oppenheimer, Fritz Diekmann
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.