Identification of high-risk COVID-19 patients using machine learning

PLoS One. 2021 Sep 20;16(9):e0257234. doi: 10.1371/journal.pone.0257234. eCollection 2021.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • COVID-19 / diagnosis*
  • Female
  • Humans
  • Logistic Models
  • Machine Learning
  • Male
  • Middle Aged
  • Neural Networks, Computer
  • Risk Factors
  • Support Vector Machine
  • Young Adult

Grants and funding

This study was supported by Consejo Nacional de Ciencia y Tecnología Mexico (CONACyT) in the form of a grant awarded to RJLM and MAQJ (CB-2016-01/284372), Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México (DGAPA-UNAM) in the form of grants awarded to RJLM and MAQJ (UNAM-PAPIIT IN102920) and AU (UNAM-PAPIIT IN103521), and Air Force Office of Scientific Research (AFOSR) in the form of a grant awarded to AU (FA9550-21-1-0147).