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Article

Machine Learning for Mortality Analysis in Patients with COVID-19

1
Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
2
IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain
3
Department of Physiotherapy, Universitat de Valencia, 46010 Valencia, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Environ. Res. Public Health 2020, 17(22), 8386; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228386
Received: 1 September 2020 / Revised: 4 November 2020 / Accepted: 6 November 2020 / Published: 12 November 2020
(This article belongs to the Special Issue The World in Crisis: Current Health Issues)
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, O2 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. View Full-Text
Keywords: COVID-19; survival analysis; machine learning; feature importance; graphical models COVID-19; survival analysis; machine learning; feature importance; graphical models
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MDPI and ACS Style

Sánchez-Montañés, M.; Rodríguez-Belenguer, P.; Serrano-López, A.J.; Soria-Olivas, E.; Alakhdar-Mohmara, Y. Machine Learning for Mortality Analysis in Patients with COVID-19. Int. J. Environ. Res. Public Health 2020, 17, 8386. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228386

AMA Style

Sánchez-Montañés M, Rodríguez-Belenguer P, Serrano-López AJ, Soria-Olivas E, Alakhdar-Mohmara Y. Machine Learning for Mortality Analysis in Patients with COVID-19. International Journal of Environmental Research and Public Health. 2020; 17(22):8386. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228386

Chicago/Turabian Style

Sánchez-Montañés, Manuel, Pablo Rodríguez-Belenguer, Antonio J. Serrano-López, Emilio Soria-Olivas, and Yasser Alakhdar-Mohmara. 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19" International Journal of Environmental Research and Public Health 17, no. 22: 8386. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17228386

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