Machine Learning for Mortality Analysis in Patients with COVID-19.

Journal: International journal of environmental research and public health
Published Date:

Abstract

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.

Authors

  • Manuel Sánchez-Montañés
    Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain.
  • Pablo Rodríguez-Belenguer
    IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain.
  • Antonio J Serrano-López
    IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain.
  • Emilio Soria-Olivas
    Intelligent Data Analysis Laboratory, Electronic Engineering Department, Universitat de València, Valencia, Spain.
  • Yasser Alakhdar-Mohmara
    Department of Physiotherapy, Universitat de Valencia, 46010 Valencia, Spain.