Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data

Abstract

Objective: To predict COVID-19 in hospitalized patients with SARS in a city in southern Brazil by using machine learning algorithms. Methods: The study sample consisted of patients ≥ 18 years of age admitted to the emergency department with SARS and hospitalized in the Hospital Escola-Universidade Federal de Pelotas between March and December of 2020. Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. Mean AUC values were calculated for each combination of model and oversampling/undersampling techniques during cross-validation. Results: Of a total of 100 hospitalized patients with SARS, 78 had information for RT-PCR testing for SARS-CoV-2 infection and were therefore included in the analysis. Most (58%) of the patients were female, and the mean age was 61.4 ± 15.8 years. Regarding the machine learning models, the random forest model had a slightly higher median performance when compared with the other models tested and was therefore adopted. The most important features to diagnose COVID-19 were leukocyte count, PaCO2, troponin levels, duration of symptoms in days, platelet count, multimorbidity, presence of band forms, urea levels, age, and D-dimer levels, with an AUC of 87%. Conclusions: Artificial intelligence techniques represent an efficient strategy to identify patients with high clinical suspicion, particularly in situations in which health care systems face intense strain, such as in the COVID-19 pandemic.