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 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.

Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada’s second COVID-19 wave

“…Moreover, moving forward, it will also be important to consider how wearable sensor data can be linked with other health data such as laboratory tests to yield more impactful diagnoses, to address potential issues with data format and secure storage with an eye to heightened challenges in resource-constrained settings, and to ensure that device users prompted to quarantine have appropriate supports to do so [ 30 , 52 , 53 ]. Ultimately, as sensor technology and detection algorithms evolve–for example, to potentially distinguish infections with SARS-CoV-2 from those with seasonal influenza–there is clear merit to further exploring how wearable sensors can be incorporated into FTTI systems to support pandemic mitigation [ 54 ].…”

Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database

“…B ) 1 (Zoabi, Menni) [25,26]. B ) 1 (Yanamala) [27]. D ) 1 (Gozes, Song, Jin, Punn) [28][29][30][31].…”

An adaptive ml model for covid-19 diagnosis in a smart hospital environment

“…These estimated dangers emanating from the pandemic have stirred the need for intelligent predictive systems to automate the healthcare delivery processes [3]. According to [4] all of this is due to a lack of frontline diagnostic systems or models integrated into healthcare systems to support healthcare professional or workers in detect, diagnose or detect the order of healthcare provision to patients are real in the healthcare sector especially in scenarios where viral infections start begins circulating with in our communities. The lack of this health support systems requires an adaptive approach to research in the areas of healthcare support system in order to re-collect and re-organised the data of patients and re-train the previously learnt models according to current environment changes to produce the best of systems for effective diagnostic and prediction models to support healthcare delivery.…”