Machine Learning for Mortality Analysis in Patients with COVID-19

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.

An Online Risk Calculator for Rapid Prediction of In-hospital Mortality from COVID-19 Infection

“…As expected, mortality also increased with age and with higher BUN levels. 5,19,20 Notably, after considering other variables, race and ethnicity were not significant predictors of mortality, as has been seen in other studies. 11,21,22 While previous studies have identified other laboratory values, such as red cell distribution width and D-dimer levels, as significant predictors, they did not contribute to this algorithm.…”

“…Previous studies of predictors of dying from COVID-19 infection have had a small number of deaths; [1][2][3][4][5][6][7] used comorbid conditions, diagnoses ,and severity indices from electronic medical records that may not be known at the time of admission; 4,[8][9][10][11][12][13] or included specialized laboratory tests-such as levels of C-reactive protein, troponin, and D-dimers-that are not readily available for urgent triage of patients for hospital admission or intensive care. 14 Some studies were done in ethnically homogenous populations such as Wuhan, China 1 or Italy, 15 in specific populations such as nursing home residents, 16 or among patients already being treated in an intensive care unit (ICU) 6,9 Although some studies applied machine learning methods to develop predictive models, 1,5,17,18 none have been based on measurements available at the time of triage in a large diverse population, nor have they been translated into calculators that can be used in clinical settings.…”

Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

“…Comparatively, researchers in [ 17 ] analyzed the cases of COVID-19 in Madrid using ML and survival analysis techniques to predict mortality. The dataset was acquired from the HER system of HM hospitals and contained 29 variables from admission and clinical data from 2307 patients.…”

Deep Forest Model for Diagnosing COVID-19 From Routine Blood Tests

“…[11], [13], [31], [36], [40], [44], [49], [53] K-Nearest Neighbour (KNN) [9], [10], [27], [31], [32], [45], [49], [50], [53] Support Vector Machine (SVM) [9], [10], [27], [31], [32], [36], [44], [45], [49], [50], [53] Extreme Gradient Boosting (XGBoost) [12], [31], [33], [35], [36], [38], [39], [46], [48], [50], [52] Logistic Regression (LR) [9], [10], [12], [27], [32], [33], [34], [36], [38], [42], [43], [44],…”