Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 14;69(4):e20221394.
doi: 10.1590/1806-9282.20221394. eCollection 2023.

An online platform for COVID-19 diagnostic screening using a machine learning algorithm

Erito Marques de Souza Filho  1 , Rodrigo de Souza Tavares  1 , Bruno José Dembogurski  1 , Alice Helena Nora Pacheco Gagliano  2 , Luiz Carlos de Oliveira Pacheco  2 , Luiz Gabriel de Resende Nora Pacheco  2 , Filipe Braida do Carmo  1 , Leandro Guimarães Marques Alvim  1 , Alexandra Monteiro  3
Affiliations

An online platform for COVID-19 diagnostic screening using a machine learning algorithm

Erito Marques de Souza Filho et al. Rev Assoc Med Bras (1992). .

Abstract

Objective: COVID-19 has brought emerging public health emergency and new challenges. It configures a complex panorama that has been requiring a set of coordinated actions and has innovation as one of its pillars. In particular, the use of digital tools plays an important role. In this context, this study presents a screening algorithm that uses a machine learning model to assess the probability of a diagnosis of COVID-19 based on clinical data.

Methods: This algorithm was made available for free on an online platform. The project was developed in three phases. First, an machine learning risk model was developed. Second, a system was developed that would allow the user to enter patient data. Finally, this platform was used in teleconsultations carried out during the pandemic period.

Results: The number of accesses during the period was 4,722. A total of 126 assistances were carried out from March 23, 2020, to June 16, 2020, and 107 satisfaction survey returns were received. The response rate to the questionnaires was 84.92%, and the ratings obtained regarding the satisfaction level were higher than 4.8 (on a 0-5 scale). The Net Promoter Score was 94.4.

Conclusion: To the best of our knowledge, this is the first online application of its kind that presents a probabilistic assessment of COVID-19 using machine learning models exclusively based on the symptoms and clinical characteristics of users. The level of satisfaction was high. The integration of machine learning tools in telemedicine practice has great potential.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest: the authors declare there is no conflicts of interest.

Figures

Figure 1
Figure 1. Machine learning tool presentation screen.
Figure 2
Figure 2. Output screen with probability estimation.
Figure 3
Figure 3. Evaluation of five aspects of care and Net Promoter Score.
See this image and copyright information in PMC

References

    1. Provenzano BC, Bartholo T, Ribeiro-Alves M, Santos APGD, Mafort TT, Castro MCS, et al. The impact of healthcare-associated infections on COVID-19 mortality: a cohort study from a Brazilian public hospital. Rev Assoc Med Bras (1992) 2021;67(7):997–1002. doi: 10.1590/1806-9282.20210433. - DOI - PubMed
    1. Mintz J, Labiste C, DiCaro MV, McElroy E, Alizadeh R, Xu K. Teleophthalmology for age-related macular degeneration during the COVID-19 pandemic and beyond. J Telemed Telecare. 2020;28 doi: 10.1177/1357633X20960636. 1357633X20960636. - DOI - PMC - PubMed
    1. Souza EM, Filho, Fernandes FA, Soares CLA, Seixas FL, Santos AASMDD, Gismondi RA, et al. Artificial intelligence in cardiology: concepts, tools and challenges - “the horse is the one who runs, you must be the jockey”. Arq Bras Cardiol. 2020;114(4):718–725. doi: 10.36660/abc.20180431. - DOI - PMC - PubMed
    1. Than MP, Pickering JW, Sandoval Y, Shah ASV, Tsanas A, Apple FS, et al. Machine learning to predict the likelihood of acute myocardial infarction. Circulation. 2019;140(11):899–909. doi: 10.1161/CIRCULATIONAHA.119.041980. - DOI - PMC - PubMed
    1. Kwon JM, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc. 2018;7(13):e008678. doi: 10.1161/JAHA.118.008678. - DOI - PMC - PubMed