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Discretionary Corrigenda (188)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/110851, first published .
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Correction: Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data

Correction: Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data

Authors of this article:

Yujeong Jang1 Author Orcid Image ;   Jae Hoon Kwon1 Author Orcid Image ;   Heeyong Kim2 Author Orcid Image ;   You-Jin Joung1, 3 Author Orcid Image ;   Junho ‍Nang4 Author Orcid Image ;   Chang Hyun Kim2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Regulatory Science, Kyung Hee University, Seoul, Republic of Korea

    2Department of Surgery, Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun, Republic of Korea

    3C&R Research, Seoul, Republic of Korea

    4Clinical Trial Center, Institute for Biomedical Science, Chonnam National University Hwasun Hospital, 322, Seoyang-ro, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea

    *these authors contributed equally

    Corresponding Author:

    Junho ‍Nang, PhD



    In “Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data” [1], the authors noted one error.

    The Funding section was revised from the following:

    This research was supported by the Korea-Hwasun Oncology Precision Biomedicine & Experimental Trials Project, a research and development project commissioned by the Ministry of Health and Welfare of the Republic of Korea (grant RS-2025-00433283), and by the Development of an AI-Driven Platform for Clinical Validation in Senior Healthcare Focused on Degenerative Brain Diseases through the Korea Planning and Evaluation Institute of Industrial Technology (grant RS-2024-00433283).

    The corrected Funding section now reads:

    This research was supported by the SmartTech Clinical Research Center under the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant RS-2025‐19252970), and by the Development of an AI-Driven Platform for Clinical Validation in Senior Healthcare Focused on Degenerative Brain Diseases through the Korea Planning and Evaluation Institute of Industrial Technology (grant RS-2024‐00433283).

    The correction will appear in the online version of the paper on the JMIR Publications website 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.

    1. Jang Y, Kwon JH, Kim H, Joung YJ, Nang J, Kim CH. Evaluation of the applicability of synthetic data in the development of colorectal cancer survival prediction models: external validation of advanced machine learning models based on National Cancer Data Center data. J Med Internet Res. Jul 7, 2026;28:e86087. [CrossRef] [Medline]

    This is a non–peer-reviewed article. submitted 31.Aug.2026; accepted 21.Sep.2026; published 30.Oct.2026.

    Copyright

    © Junho ‍Nang, Chang Hyun Kim, Yujeong Jang, Jae Hoon Kwon, Heeyong Kim, You-Jin Joung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.Oct.2026.

    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 (ISSN 1438-8871), 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.