A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients
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 datas...
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Nature Portfolio
2021
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oai:doaj.org-article:72d1d24198684f2fb4d0f2544abe5ac22021-12-02T18:24:55ZA vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients10.1038/s41746-021-00467-82398-6352https://doaj.org/article/72d1d24198684f2fb4d0f2544abe5ac22021-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00467-8https://doaj.org/toc/2398-6352Abstract 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.Naveena YanamalaNanda H. KrishnaQuincy A. HathawayAditya RadhakrishnanSrinidhi SunkaraHeenaben PatelPeter FarjoBrijesh PatelPartho P. SenguptaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021) |
| institution |
DOAJ |
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DOAJ |
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EN |
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Computer applications to medicine. Medical informatics R858-859.7 |
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Computer applications to medicine. Medical informatics R858-859.7 Naveena Yanamala Nanda H. Krishna Quincy A. Hathaway Aditya Radhakrishnan Srinidhi Sunkara Heenaben Patel Peter Farjo Brijesh Patel Partho P. Sengupta A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| description |
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. |
| format |
article |
| author |
Naveena Yanamala Nanda H. Krishna Quincy A. Hathaway Aditya Radhakrishnan Srinidhi Sunkara Heenaben Patel Peter Farjo Brijesh Patel Partho P. Sengupta |
| author_facet |
Naveena Yanamala Nanda H. Krishna Quincy A. Hathaway Aditya Radhakrishnan Srinidhi Sunkara Heenaben Patel Peter Farjo Brijesh Patel Partho P. Sengupta |
| author_sort |
Naveena Yanamala |
| title |
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| title_short |
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| title_full |
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| title_fullStr |
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| title_full_unstemmed |
A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients |
| title_sort |
vital sign-based prediction algorithm for differentiating covid-19 versus seasonal influenza in hospitalized patients |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/72d1d24198684f2fb4d0f2544abe5ac2 |
| work_keys_str_mv |
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