Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems

Abstract: The course of the COVID-19 pandemic imposes a significant burden on healthcare systems, including on primary care, when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID-19 impose difficulties in identifying suspicions. To improve the definition of COVID-19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management.
The scientific analysis shows the manifestations versatility and the occurrence frequency COVID-19. We structured the manifestations by occurrence frequency, classified them as “large” and “small”. The rules for their interaction were determined to calculate the level of suspicion for COVID-19. Recommendations on patient management tactics were developed for each level of suspicion. NLP models were trained to identify the symptoms of COVID-19 in the unstructured texts of electronic health records. The accuracy of the models on the F-measure metric ranged from 84.6% to 96.0%. Thus, a COVID-19 prediction method was developed, which can be used in symptom checkers and MDSS to help doctors determine COVID-19 and support tactical actions.