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 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.

Keywords: Artificial neural network; COVID-19; Data envelopment analysis; Kidney transplant; Logistic regression; Random forest.

Fig. 1

Transplant recipients controlled or attended at the transplant unit. Patients were followed-up from March 3th to April 24th, 2020, as either regular follow-ups, or contacted due to COVID-19 suspicion or other reasons. All patients were asked to report suspicious symptoms. Only patients with high evidence of SARS-CoV-19 infection requiring hospital admission were included in the analysis

Fig. 2

Implementation and evaluation stages of the selection process: hybrid model versus alternative configurations

Fig. 3

Indexing effect across input variables. The red circles represent the value of the triples described by the data. The blue circles correspond to the index values generated after implementing DEA

Fig. 4

Patient performance across input variables for the different index categories generated using DEA. The index has been normalized within the interval [0, 1]. Higher values represent relatively worse performances

Fig. 5

Quartile setting. CNF3 configuration

Fig. 6

Tercile setting.

Fig. 6

Tercile setting.

Fig. 6

Tercile setting.