Identification of high-risk COVID-19 patients using machine learning
Por:
Quiroz-Juarez, Mario A., Torres-Gomez, Armando, Hoyo-Ulloa, Irma, de León-Montiel R.D.J., U'Ren, Alfred B.
Publicada:
20 sep 2021
Categoría:
Multidisciplinary
Resumen:
The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe
acute respiratory syndrome coronavirus 2), has produced a devastating
toll both in terms of human life loss and economic disruption. In this
paper we present a machine-learning algorithm capable of identifying
whether a given patient (actually infected or suspected to be infected)
is more likely to survive than to die, or vice-versa. We train this
algorithm with historical data, including medical history, demographic
data, as well as COVID-19-related information. This is extracted from a
database of confirmed and suspected COVID-19 infections in Mexico,
constituting the official COVID-19 data compiled and made publicly
available by the Mexican Federal Government. We demonstrate that the
proposed method can detect high-risk patients with high accuracy, in
each of four identified clinical stages, thus improving hospital
capacity planning and timely treatment. Furthermore, we show that our
method can be extended to provide optimal estimators for
hypothesis-testing techniques commonly-used in biological and medical
statistics. We believe that our work could be of use in the context of
the current pandemic in assisting medical professionals with real-time
assessments so as to determine health care priorities.
Filiaciones:
Quiroz-Juarez, Mario A.:
Departamento de Física, Universidad Autónoma Metropolitana Unidad Iztapalapa, Ciudad de México, Mexico
Torres-Gomez, Armando:
ABC Medical Center, Ciudad de México, Mexico
Hoyo-Ulloa, Irma:
ABC Medical Center, Ciudad de México, Mexico
de León-Montiel R.D.J.:
Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de Mexico, Ciudad de Mexico, Mexico
U'Ren, Alfred B.:
Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de Mexico, Ciudad de Mexico, Mexico
gold, Green Published, Green Submitted, Gold, Green
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