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Machine Learning for Mortality Analysis in Patients with COVID-19

Author

Listed:
  • Manuel Sánchez-Montañés

    (Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain
    These authors contributed equally to this work.)

  • Pablo Rodríguez-Belenguer

    (IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain
    These authors contributed equally to this work.)

  • Antonio J. Serrano-López

    (IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain
    These authors contributed equally to this work.)

  • Emilio Soria-Olivas

    (IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Valencia, 46100 Burjassot, Spain
    These authors contributed equally to this work.)

  • Yasser Alakhdar-Mohmara

    (Department of Physiotherapy, Universitat de Valencia, 46010 Valencia, Spain
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Manuel Sánchez-Montañés & Pablo Rodríguez-Belenguer & Antonio J. Serrano-López & Emilio Soria-Olivas & Yasser Alakhdar-Mohmara, 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8386-:d:444148
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    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Irfan Ullah Khan & Nida Aslam & Malak Aljabri & Sumayh S. Aljameel & Mariam Moataz Aly Kamaleldin & Fatima M. Alshamrani & Sara Mhd. Bachar Chrouf, 2021. "Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients," IJERPH, MDPI, vol. 18(12), pages 1-20, June.

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