A Hybrid Data Envelopment Analysis-Artificial Neural Network (DEANN) Technique for Extrapolating the Evolution of Kidney Transplant Patients

  1. Santos Arteaga, Francisco Javier 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Revista:
AMERICAN JOURNAL OF TRANSPLANTATION

ISSN: 1600-6143

Año de publicación: 2020

Tipo: Artículo

DOI: 10.1007/S10462-021-10008-0 GOOGLE SCHOLAR

Otras publicaciones en: AMERICAN JOURNAL OF TRANSPLANTATION

Resumen

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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