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

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

SUBMITTER: Revuelta I 

PROVIDER: S-EPMC8062617 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature