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

Revuelta, Ignacio; Santos-Arteaga, Francisco J.; Montagud-Marrahi, Enrique; Ventura-Aguiar, Pedro; Di Caprio, Debora; Cofan, Frederic; Cucchiari, David; Torregrosa, Vicens; Piñeiro, Gaston Julio; Esforzado, Nuria; Bodro, Marta; Ugalde-Altamirano, Jessica; Moreno, Asuncion; Campistol, Josep M.; Alcaraz, Antonio; Bayès, Beatriu; Poch, Esteban; Oppenheimer, Federico; Diekmann, Fritz

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.