Machine Learning for Mortality Analysis in Patients with COVID-19

Manuel Sánchez-Montañés, Pablo Rodríguez-Belenguer, Antonio J. Serrano-López, Emilio Soria-Olivas and Yasser Alakhdar-Mohmara
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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: IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain
Yasser Alakhdar-Mohmara: Department of Physiotherapy, Universitat de Valencia, 46010 Valencia, Spain

IJERPH, 2020, vol. 17, issue 22, 1-20

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 2 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.

Keywords: COVID-19; survival analysis; machine learning; feature importance; graphical models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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