CORRECTION article
Front. Cardiovasc. Med.
Sec. General Cardiovascular Medicine
Correction:Construction of a risk prediction model for postoperative atrial fibrillation in lung cancer patients based on multidimensionalfeature fusion and ensemble learning
Provisionally accepted- 1Xinjiang Medical University College of Public Health, 823037, Urumqi, China
- 2Cancer Hospital of Xinjiang Medical University, Urumqi, China
- 3The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, China
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A correction refers to a change to their article that the author wishes to publish after publication. The publication of this article is subject to Frontiers' editorial approval. • Please read through all the templates before choosing • Pick the most relevant text template(s) from the following page and delete all others.• Edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below. • Please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary • Submit to Frontiers following the instructions on this page.When the original text contained incorrect information, to preserve the scientific record, please include that text when editing the below templates. For example:There was a mistake in the Funding statement, an incorrect number was used.The correct number is "2015C03Bd051.". The publisher apologizes for this mistake.The original version of this article has been updated.In the published article, there was a mistake in the Funding statement. The funding statement for the Key Development Project of the Department of Science and Technology was displayed as "2015CBd051". The correct statement is "Key Development Project of Department of Science and Technology (2015C03Bd051).'' Name of all authors as they appear in the published original article (Ziwei Gong 1 , Silamuguli Haierla 2 , Jing Shi 2 and Xinya Liu 3* )
Keywords: lung cancer, machine learning, postoperative atrial fibrillation, Risk factors, Prediction model
Received: 10 Nov 2025; Accepted: 11 Nov 2025.
Copyright: © 2025 Gong, Haierla, Shi and Liu. 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) or licensor 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:
Ziwei Gong, gzw991107@163.com
Xinya Liu, 13899935339@163.com
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