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CORRECTION article

Front. Psychiatry, 28 January 2026

Sec. Mood Disorders

Volume 17 - 2026 | https://doi.org/10.3389/fpsyt.2026.1787831

Correction: Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning

This article is a correction to:

  1. Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning

    1. Read original article
Jingyuan Lin*&#x;Jingyuan Lin1*†Jiansong Yu&#x;Jiansong Yu2†
  • 1Department of Rehabilitation Medicine, Fujian Provincial Geriatric Hospital, Fuzhou, China
  • 2Department of Rehabilitation Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China

A Correction on
Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning

by Lin J and Yu J (2026) Front. Psychiatry 16:1722447. doi: 10.3389/fpsyt.2025.1722447

Author Jiansong Yu was erroneously omitted as equal contributing author.

The original version of this article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: cognitive behavioral therapy (CBT), machine learning, post-stroke depression (PSD), predictive modeling, retrospective analysis

Citation: Lin J and Yu J (2026) Correction: Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning. Front. Psychiatry 17:1787831. doi: 10.3389/fpsyt.2026.1787831

Received: 14 January 2026; Accepted: 16 January 2026; Revised: 15 January 2026;
Published: 28 January 2026.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2026 Lin and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jingyuan Lin, MTAyNzMzNjMwMUBxcS5jb20=

†These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.