4.6 Article

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

Journal

ARTIFICIAL INTELLIGENCE REVIEW
Volume 54, Issue 6, Pages 4653-4684

Publisher

SPRINGER
DOI: 10.1007/s10462-021-10008-0

Keywords

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

Funding

  1. Redes Tematicas de Investigacion Cooperativa en Salud, REDINREN, from the Instituto de Salud Carlos III-Ministerio de Ciencia e Innovacion [RD06/0016/1002, RD12/0021/0028]
  2. Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa

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In response to overwhelming health emergencies, accurate prediction models using scientific evidence are essential for guiding healthcare centers, especially for high-risk populations. The developed hybrid prediction model offers high accuracy in predicting severe COVID-19 progression, outperforming other competing models and assisting in patient-centered resource management for COVID-19.
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.

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