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, 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.
Highlights
The COVID-19 pandemic is a great challenge for humanity, with more than 25 million confirmed cases as of 31 August 2020 according to the World Health Organization (WHO) [1]
Interpretable decision rules for estimating the risk of mortality of the patient can be obtained from the decision trees, which can be crucial in the prioritization of medical care and resources
There is a generalized collapse of health systems, and projects such as COVID DATA SAVE LIVES are essential for artificial intelligence to provide healthcare staff with tools to speed up decision-making
Summary
The COVID-19 pandemic is a great challenge for humanity, with more than 25 million confirmed cases as of 31 August 2020 according to the World Health Organization (WHO) [1]. A search, on the same date, on clinicaltrials.gov for studies targeting the COVID condition showed more than 3100 registered studies [2,3] This disease has affected the whole world with a large number of studies and review articles appearing about different aspects of this disease: possible symptoms and treatments [4,5,6,7,8,9], technological tools to combat the virus [10,11,12,13], epidemiological models of virus transmission [14,15,16], the detection of fake news related to COVID-19 [17,18], etc. The number of published papers about COVID-19 is continuously growing [31,32,33]
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