Journal article
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients
NPJ digital medicine, Vol.4(1), 95
06/04/2021
DOI: 10.1038/s41746-021-00467-8
PMCID: PMC7814848
PMID: 33469602
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 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.
Details
- Title: Subtitle
- A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients
- Creators
- Naveena Yanamala - West Virginia UniversityNanda H. Krishna - West Virginia UniversityQuincy A. Hathaway - West Virginia UniversityAditya Radhakrishnan - West Virginia UniversitySrinidhi Sunkara - West Virginia UniversityHeenaben Patel - West Virginia UniversityPeter Farjo - West Virginia UniversityBrijesh Patel - West Virginia UniversityPartho P. Sengupta - West Virginia University
- Resource Type
- Journal article
- Publication Details
- NPJ digital medicine, Vol.4(1), 95
- Publisher
- NATURE PORTFOLIO
- DOI
- 10.1038/s41746-021-00467-8
- PMID
- 33469602
- PMCID
- PMC7814848
- ISSN
- 2398-6352
- eISSN
- 2398-6352
- Number of pages
- 10
- Grant note
- 5U54GM104942-04 / National Institute of General Medical Sciences of the National Institutes of Health under (NIH) 1920920 / National Science Foundation (NSF)
- Language
- English
- Date published
- 06/04/2021
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984694747102771
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