Prediction of COVID-19 Diagnosis Based on OpenEHR Artefacts

The complexity and momentum of monitoring COVID-19 patients calls for the usage of agile and scalable data structure methodologies. A system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 infected patients was developed based on the openEHR architecture. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different machine learning algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, and a specificity of 99.92%, using the Decision Tree algorithm and the Split Validation method.