Selected article for: "decision tree and logistic regression"
Author: Sánchez-Montañés, Manuel; Rodríguez-Belenguer, Pablo; Serrano-López, Antonio J.; Soria-Olivas, Emilio; Alakhdar-Mohmara, Yasser
Title: Machine Learning for Mortality Analysis in Patients with COVID-19
  • Cord-id: xlciu6kg
  • Document date: 2020_11_12
    • ID: xlciu6kg
    Snippet: 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. I
    Document: 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.

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