Abstract
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.
Highlights
Infection with severe acute respiratory syndrome coronavirus 2(SARS-CoV 2) causing coronavirus disease 2019 (COVID-19) has led to an unprecedented global crisis due to its vigorous transmission, spectrum of respiratory manifestations, and vascular affects[1,2,3].The etiology of the disease is further complicated by a diverse set of clinical presentations, ranging from asymptomatic to progressive viral pneumonia and mortality[4]
While the use of machine learning has been applied to contact tracing and forecasting during the COVID-19 epidemic[8], it has only limitedly been explored as a means for accurately predicting COVID-19 infection on clinical presentation
Preliminary work has shown the utility of machine and deep learning algorithms in predicting COVID-19 for patient features[9,10,11] and on CT examination[12,13], but there remains a paucity in research showing the capacity of machine learning algorithms in differentiating between COVID-19 and influenza patients
Summary
Infection with severe acute respiratory syndrome coronavirus 2(SARS-CoV 2) causing coronavirus disease 2019 (COVID-19) has led to an unprecedented global crisis due to its vigorous transmission, spectrum of respiratory manifestations, and vascular affects[1,2,3].The etiology of the disease is further complicated by a diverse set of clinical presentations, ranging from asymptomatic to progressive viral pneumonia and mortality[4]. Due to its similar symptomatology, COVID-19 has drawn comparisons to the seasonal influenza epidemic[5] Both infections commonly present with overlapping symptoms, leading to a clinical dilemma for clinicians as SARS-CoV 2 carries a case-fatality rate up to 30 times that of influenza and infects healthcare workers at a significantly higher rate[3,6,7]. The concurrence of epidemics appears imminent as the considerable COVID-19 incidence continues and even a moderate influenza season would result in over 35 million cases and 30,000 deaths[5,6] To help curb this dilemma, front-line providers need the ability to rapidly and accurately triage these patients. Preliminary work has shown the utility of machine and deep learning algorithms in predicting COVID-19 for patient features[9,10,11] and on CT examination[12,13], but there remains a paucity in research showing the capacity of machine learning algorithms in differentiating between COVID-19 and influenza patients
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