https://www.socictopen.socict.org/files/original/84ce66063fe41c89f3f9c7c960dec2a2.pdf 048566f9b30fb1866c276b54889365ab Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of COVID-19 From Hemogram Results and Age Using Machine Learning Creator An entity primarily responsible for making the resource Elena Caires Silveira Description An account of the resource Introduction: The rapid global dissemination of COVID-19 culminated in the mobilization of great technological efforts aimed at its better understanding and control. In this context, Machine Learning gains notoriety, and its application has been widely documented for pathophysiological, diagnostic, therapeutic, prognostic and monitoring of COVID-19 purposes. The present study aimed to build a model for the prediction of the diagnosis of COVID-19 based on blood count results and age of patients and to identify the main characteristics taken into account by the algorithm for the predictive decision. Material and Methods: Anonymous data from 1157 patients made available by the COVID-19 Data Sharing / BR repository were used. The work took place in two distinct stages: description and analysis of the data; and construction of the predictive model. Results: With the exception of hemoglobin measurement, mean corpuscular volume, red cell distribution width, mean platelet volume and neutrophil-lymphocyte ratio, there was a statistically significant association of all other hematological parameters assessed with COVID-19. The predictive model developed from the XGBoost classifier reached an accuracy of 80.0% with a sensitivity of 75.6% and specificity of 82.0%. The variables that had the greatest influence on the predictive decision were basophil, eosinophil and leukocyte measurements. The present study confirms the potential of using blood count results, a widely available and accessible test, in the context of the diagnostic evaluation and pathophysiological investigation of COVID-19. Conclusion: This work highlights the relevance of the systematization and dissemination of data related to COVID-19 for use in new research. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context 10.30699/fhi.v9i1.234 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Computer applications to medicine. Medical informatics, Bibliography. Library science. Information resources