A novel high specificity COVID-19 screening method based on simple blood exams and artificial intelligence

Felipe Soares; Aline Villavicencio; Michel Jose Anzanello; Flavio Sanson Fogliatto; Marco Idiart; Mark Stevenson

We developed our solution for patients arriving at a hospital's emergency room. In Figure 1 we depict the workflow for using the proposed Artificial Intelligence (AI) model. c https://www.kaggle.com/einsteindata4u/covid19 d https://medicalsuite.einstein.br/pratica-medica/Documentos%20Doencas%20Epidemicas/Manejode-casos-suspeitos-de-sindrome-respiratoria-pelo-COVID-19.pdf -In Portuguese e https://www.who.int/publications-detail/global-surveillance-for-human-infection-with-novelcoronavirus-(2019-ncov) f https://portalarquivos2.saude.gov.br/images/pdf/2020/marco/04/2020-03-02-Boletim-Epidemiol--gico-04-corrigido.pdf A patient would initially be evaluated by health professionals to determine if she/he is a probable case of COVID-19. Once the patient is determined as a suspect case, simple blood exams would be requested, since they are the main input in the ER-CoV artificial intelligence model (see the previous section). Upon data entry (blood exams and age), the ER-CoV model yields a positive or negative result. In case of a positive result, considering the model's relatively low sensitivity, priority should be given to this patient for further investigation including confirmatory PCR or antibody-based test for SSARS-CoV-2 and CT scan. If the result is negative, the patient is very likely not to have COVID-19.