JMIR J Med Internet Res Journal of Medical Internet Research 1438-8871 JMIR Publications Toronto, Canada v25i1e55010 38064711 10.2196/55010 Corrigenda and Addenda Corrigenda and Addenda Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post–COVID-19 Condition: Natural Language Processing Approach Dolatabadi Elham BSc, MSc, PhD 1
Faculty of Health, School of Health Policy and Management York University 4700 Keele Street North York Toronto, ON, M3J 1P3 Canada 1 6477069756 edolatab@yorku.ca
2 3 https://orcid.org/0000-0003-2236-2611 Moyano Diana BBA, MMAI 2 https://orcid.org/0009-0007-9865-2232 Bales Michael BASc, BSc, MBA 4 https://orcid.org/0009-0008-5604-2257 Spasojevic Sofija BSc, MSc, PhD 4 https://orcid.org/0000-0001-8111-4197 Bhambhoria Rohan BTech, MEng, PhD 5 https://orcid.org/0000-0002-2597-670X Bhatti Junaid MSc, PhD 6 https://orcid.org/0000-0002-9540-845X Debnath Shyamolima BSc, MSc 7 https://orcid.org/0009-0009-5062-5651 Hoell Nicholas BA, PhD 7 https://orcid.org/0009-0008-1151-4716 Li Xin BASc 3 https://orcid.org/0009-0008-1831-7459 Leng Celine BBA 4 https://orcid.org/0000-0001-9153-2958 Nanda Sasha MSc 7 https://orcid.org/0009-0005-2182-1486 Saab Jad BACS, MSc 8 https://orcid.org/0009-0007-3667-7645 Sahak Esmat BASc 3 https://orcid.org/0009-0008-3952-7068 Sie Fanny BSc, MMI 4 https://orcid.org/0009-0008-4944-2447 Uppal Sara BEng, MSc 8 https://orcid.org/0009-0006-9008-005X Vadlamudi Nirma Khatri MPH, PhD 9 https://orcid.org/0000-0001-5513-7926 Vladimirova Antoaneta MSc, PhD 10 https://orcid.org/0009-0000-5123-5158 Yakimovich Artur PhD 11 https://orcid.org/0000-0003-2458-4904 Yang Xiaoxue HBSc, MSc 7 https://orcid.org/0009-0004-9059-9017 Kocak Sedef Akinli BS, MSc, PhD 2 https://orcid.org/0009-0009-6271-415X Cheung Angela M BA, MD, PhD 3 12 https://orcid.org/0000-0001-8332-0744 Faculty of Health, School of Health Policy and Management York University Toronto, ON Canada Vector Institute Toronto, ON Canada Department of Medicine and Joint Department of Medical Imaging, University of Toronto Toronto, ON Canada Hoffmann-La Roche Ltd Toronto, ON Canada Electrical and Computer Engineering, Queen’s University Kingston, ON Canada Manulife Toronto, ON Canada Deloitte Toronto, ON Canada TELUS Health Montreal, QC Canada Department of Pediatrics, Faculty of Medicine, University of British Columbia Vancouver, BC Canada Roche Information Solutions San Francisco, CA United States Hoffmann-La Roche Ltd Munich Germany University Health Network Toronto, ON Canada Corresponding Author: Elham Dolatabadi edolatab@yorku.ca 2023 8 12 2023 25 e55010 4 12 2023 5 12 2023 ©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. 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.

 https://www.jmir.org/2023/1/e45767

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.

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.

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.

Originally published Figures 2 and 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”.