This paper is in the following e-collection/theme issue:

Discretionary Corrigenda (87)

Published on in Vol 25 (2023)

This is a member publication of University of Toronto

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55010, first published .
Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach

Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach

Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach

Authors of this article:

Elham Dolatabadi1, 2, 3 Author Orcid Image ;   Diana Moyano2 Author Orcid Image ;   Michael Bales4 Author Orcid Image ;   Sofija Spasojevic4 Author Orcid Image ;   Rohan Bhambhoria5 Author Orcid Image ;   Junaid Bhatti6 Author Orcid Image ;   Shyamolima Debnath7 Author Orcid Image ;   Nicholas Hoell7 Author Orcid Image ;   Xin Li3 Author Orcid Image ;   Celine Leng4 Author Orcid Image ;   Sasha Nanda7 Author Orcid Image ;   Jad Saab8 Author Orcid Image ;   Esmat Sahak3 Author Orcid Image ;   Fanny Sie4 Author Orcid Image ;   Sara Uppal8 Author Orcid Image ;   Nirma Khatri Vadlamudi9 Author Orcid Image ;   Antoaneta Vladimirova10 Author Orcid Image ;   Artur Yakimovich11 Author Orcid Image ;   Xiaoxue Yang7 Author Orcid Image ;   Sedef Akinli Kocak2 Author Orcid Image ;   Angela M Cheung3, 12 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Corrigenda and Addenda

    1Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada

    2Vector Institute, Toronto, ON, Canada

    3Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    4Hoffmann-La Roche Ltd, Toronto, ON, Canada

    5Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada

    6Manulife, Toronto, ON, Canada

    7Deloitte, Toronto, ON, Canada

    8TELUS Health, Montreal, QC, Canada

    9Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

    10Roche Information Solutions, San Francisco, CA, United States

    11Hoffmann-La Roche Ltd, Munich, Germany

    12University Health Network, Toronto, ON, Canada

    Corresponding Author:

    Elham Dolatabadi, BSc, MSc, PhD

    Faculty of Health, School of Health Policy and Management

    York University

    4700 Keele Street

    North York

    Toronto, ON, M3J 1P3

    Canada

    Phone: 1 6477069756

    Email: edolatab@yorku.ca



    In “Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach” (J Med Internet Res 2023;25:e45767) the authors noted two errors in Figure presentations of Twitter data.

    In the originally published article, in Figure 2B the presentation of the occurrence frequency of Grouped terms on Twitter (the left subplot) appeared incorrectly. This subplot has been corrected and the accurate presentation of the occurrence frequency of Grouped terms on Twitter has been shown.

    In the originally published article, in Figure 3A the presentation of the occurrence frequency of Mapped terms on Twitter (the lower left heatmap plot) appeared incorrectly. This heatmap plot has been corrected and the accurate presentation of the co-occurrence frequency of Mapped terms on Twitter has been shown.

    The originally published versions of Figures 2 and 3 are included in Multimedia Appendix 1.

    ‎
    Figure 2. The occurrence frequency of the most prevailing extracted symptom and condition terms in Twitter and Reddit data with occurrence frequency greater than 1% (n>350 for Twitter, and n>4000 for Reddit). Normalized terms are the raw terms that were normalized (after a 2-step normalization process, as shown in Figure 1) to the 203 standardized unique concepts derived from a web-based survey of 3762 patients with post–COVID-19 condition [3]. For instance, “my tiredness” is normalized into “fatigue.” Grouped terms are the normalized terms that were further categorized based on the affected organ system established by Davis et al [3]. Novel terms are the mapped terms that we had not normalized to the 203 standardized unique concepts because they were neither reported nor categorized in the survey study [3]. HEENT: head, eyes, ears, nose, and throat.
    ‎
    Figure 3. Co-occurrence frequency of normalized post–COVID-19 condition terms in Twitter (A) which is higher than 50% and Reddit (B) which is higher than 10% data. Higher values are shown by the intensity of pink and blue shading. Normalized terms are the raw terms that were normalized (after a 2-step normalization process, as shown in Figure 1) to the 203 standardized unique concepts derived from a web-based survey of 3762 patients with post–COVID-19 condition [3]. For instance, “my tiredness” is normalized into “fatigue”. Please see Multimedia Appendix 2 for a larger version.

    The correction will appear in the online version of the paper on the JMIR Publications website on December 8, 2023 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

    Multimedia Appendix 1

    Originally published Figures 2 and 3.

    DOCX File , 2363 KB
    Multimedia Appendix 2

    Co-occurrence frequency of normalized post–COVID-19 condition terms in Twitter (A) which is higher than 50% and Reddit (B) which is higher than 10% data. Higher values are shown by the intensity of pink and blue shading. Normalized terms are the raw terms that were normalized (after a 2-step normalization process, as shown in Figure 1) to the 203 standardized unique concepts derived from a web-based survey of 3762 patients with post–COVID-19 condition [3]. For instance, “my tiredness” is normalized into “fatigue”.

    PNG File , 3742 KB

    This is a non–peer-reviewed article. submitted 04.12.23; accepted 05.12.23; published 08.12.23.

    Copyright

    ©Elham Dolatabadi, Diana Moyano, Michael Bales, Sofija Spasojevic, Rohan Bhambhoria, Junaid Bhatti, Shyamolima Debnath, Nicholas Hoell, Xin Li, Celine Leng, Sasha Nanda, Jad Saab, Esmat Sahak, Fanny Sie, Sara Uppal, Nirma Khatri Vadlamudi, Antoaneta Vladimirova, Artur Yakimovich, Xiaoxue Yang, Sedef Akinli Kocak, Angela M Cheung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.12.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.