Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models
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Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning modelsAutor(es)
Fecha
2023-07Cita bibliográfica
D. I. Morís, J. de Moura, P. J. Marcos, E. M. Rey, J. Novo, y M. Ortega, «Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models», Biomedical Signal Processing and Control, vol. 84, p. 104818, jul. 2023, doi: 10.1016/j.bspc.2023.104818.
Resumen
[Abstract]: COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it.
In such situation, the clinicians must take important decisions, in an environment where medical resources can
be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of
supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the
disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several
machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information.
Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk
of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of
this work show the most relevant features for each studied scenario, as well as the classification performance
of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need
for hospitalization of a patient with an AUC-ROC of 0.8415± 0.0217 while it can also estimate the risk of death
with an AUC-ROC of 0.7992±0.0104. Results have demonstrated the great potential of the proposal to determine
those patients that need a greater amount of medical resources for being at a higher risk. This provides the
healthcare services with a tool to better manage their resources.
Palabras clave
COVID-19
Machine learning
Clinical data
Feature selection
Classification
Machine learning
Clinical data
Feature selection
Classification
Descripción
Funding for open access charge: Universidade da Coruña/CISUG.
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1746-8094