Revuelta, IgnacioSantos Arteaga, Francisco JavierDiekmann, Fritz2025-01-142025-01-142021Revuelta, I., Santos-Arteaga, F. J., Montagud-Marrahi, E., Ventura-Aguiar, P., Di Caprio, D., Cofan, F., Cucchiari, D., Torregrosa, V., Piñeiro, G. J., Esforzado, N., Bodro, M., Ugalde-Altamirano, J., Moreno, A., Campistol, J. M., Alcaraz, A., Bayès, B., Poch, E., Oppenheimer, F., & Diekmann, F. (2021). A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients. Artificial Intelligence Review, 54(6), 4653-4684. https://doi.org/10.1007/S10462-021-10008-00269-28211573-746210.1007/s10462-021-10008-0https://hdl.handle.net/20.500.14352/114135In 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, scientifc 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 confgurations 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)—Artifcial 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 identifcation of key predictors that permit a sustainable management of resources in a patient-centered model.engjournal articleopen access004.8COVID-19Kidney transplantData envelopment analysisArtifcial neural networkLogistic regressionRandom forestInteligencia artificial (Informática)Medicina1207 Investigación Operativa1203.04 Inteligencia Artificial3201 Ciencias Clínicas