Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.
【저자키워드】 Health care, Diseases, Diagnosis, 【초록키워드】 COVID-19, viral infection, Influenza, Infection, diagnostic, database, viral infections, healthcare worker, Laboratory, outcomes, Clinical course, Predictive model, healthcare, Patient, dataset, vital sign, utility, characteristic, disease, machine-learning, diagnose, AUC, Classifier, organizations, ROC AUC, internal and external, 【제목키워드】 COVID-19, Algorithm, Seasonal influenza, hospitalized patient,