A Machine Learning-Based Predictive Model for Outcome of Covid-19 in Kidney Transplant Recipients

  1. Ignacio Revuelta
  2. Francisco Javier Santos Arteaga 1
  3. Enrique Montagud-Marrahi 2
  4. Pedro Ventura Aguiar 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Hospital Clinic Barcelona
    info

    Hospital Clinic Barcelona

    Barcelona, España

    ROR https://ror.org/02a2kzf50

Actas:
American Transplant Congress

Editorial: https://publons.com/wos-op/publon/51111900/

ISSN: 1600-6135

Año de publicación: 2021

Tipo: Aportación congreso

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

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. Supplementary information: The online version contains supplementary material available at 10.1007/s10462-021-10008-0.

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