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Articles Published Processes
7/20/2026 7:08:29 AM | Browse: 2 | Download: 0
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Received |
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2026-01-26 00:39 |
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Peer-Review Started |
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2026-01-26 00:42 |
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First Decision by Editorial Office Director |
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2026-02-04 09:42 |
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Return for Revision |
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2026-02-04 09:42 |
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Revised |
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2026-02-18 19:26 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-04-16 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-04-16 07:17 |
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Articles in Press |
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2026-04-16 07:17 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-07-06 02:51 |
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Publish the Manuscript Online |
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2026-07-20 07:08 |
| ISSN |
1949-8462 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Medical Informatics |
| Manuscript Type |
Editorial |
| Article Title |
Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Mehrnaz Azarian |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Mehrnaz Azarian, MD, Post Doctoral Researcher, Postdoc, Center for Innovations in Quality, Michael E DeBakey VA Medical Center, Effectiveness and Safety, 2450 Holcombe Blvd, Suite 01Y, Houston, TX 77021, United States. mehrnaz.azarian@bcm.edu |
| Key Words |
Diabetes mellitus; Machine learning; Electrocardiography; Digital biomarkers; Single-lead electrocardiogram |
| Core Tip |
A one-minute, single-lead electrocardiogram (ECG) may enable scalable screening for diabetes mellitus (DM) when paired with machine learning. Karbovskaya et al introduced a phenotype-clustering strategy that explicitly accounts for clinical heterogeneity and cardiovascular comorbidity, revealing that DM-related ECG signatures are most detectable in specific patient subgroups rather than uniformly across populations. By emphasizing interpretable electrophysiologic features and testing model transportability across phenotypes, this work advances a “precision screening” paradigm and provides a practical roadmap for translating ECG-based metabolic risk detection into real-world cardiometabolic workflows. |
| Publish Date |
2026-07-20 07:08 |
| Citation |
Azarian M. Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening. World J Cardiol 2026; 18(7): 119396
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| URL |
https://www.wjgnet.com/1949-8462/full/v18/i7/119396.htm |
| DOI |
https://doi.org/10.4330/wjc.119396 |
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