勘误：使用机器学习研究重金属与代谢综合征之间的关系。
Corrigendum: The relationship between heavy metals and metabolic syndrome using machine learning.
作者信息
Yao Jun, Du Zhilin, Yang Fuyue, Duan Ran, Feng Tong
机构信息
Department of Respiratory and Critical Care, Guangyuan Central Hospital, Guangyuan, Sichuan, China.
Department of Oncology, Chengdu Seventh People's Hospital (Affliated Cancer Hospital of Chengdu Medical College), Chengdu, Sichuan, China.
出版信息
Front Public Health. 2024 Jul 29;12:1456139. doi: 10.3389/fpubh.2024.1456139. eCollection 2024.
Abstract
[This corrects the article DOI: 10.3389/fpubh.2024.1378041.].
摘要
[本文更正了文章的数字对象标识符：10.3389/fpubh.2024.1378041。]
相似文献
1
Corrigendum: The relationship between heavy metals and metabolic syndrome using machine learning.勘误：使用机器学习研究重金属与代谢综合征之间的关系。
Front Public Health. 2024 Jul 29;12:1456139. doi: 10.3389/fpubh.2024.1456139. eCollection 2024.
2
The relationship between heavy metals and metabolic syndrome using machine learning.基于机器学习的重金属与代谢综合征的关系。
Front Public Health. 2024 Apr 15;12:1378041. doi: 10.3389/fpubh.2024.1378041. eCollection 2024.
3
Development of an interpretable machine learning model associated with heavy metals' exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018.开发与重金属暴露相关的可解释机器学习模型，通过 SHAP 在美国成年人中识别冠心病：2003 年至 2018 年美国 NHANES 的研究结果。
Chemosphere. 2023 Jan;311(Pt 1):137039. doi: 10.1016/j.chemosphere.2022.137039. Epub 2022 Oct 29.
4
Exploring the association between two groups of metals with potentially opposing renal effects and renal function in middle-aged and older adults: Evidence from an explainable machine learning method.探讨中年和老年人中具有潜在相反肾脏作用的两组金属与肾功能之间的关联：来自可解释机器学习方法的证据。
Ecotoxicol Environ Saf. 2024 Jan 1;269:115812. doi: 10.1016/j.ecoenv.2023.115812. Epub 2023 Dec 12.
5
Analyzing the impact of heavy metal exposure on osteoarthritis and rheumatoid arthritis: an approach based on interpretable machine learning.分析重金属暴露对骨关节炎和类风湿性关节炎的影响：一种基于可解释机器学习的方法。
Front Nutr. 2024 Jul 19;11:1422617. doi: 10.3389/fnut.2024.1422617. eCollection 2024.
6
The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.与金属混合物相关的可解释机器学习模型，通过 EMR 挖掘方法识别高血压。
J Clin Hypertens (Greenwich). 2024 Feb;26(2):187-196. doi: 10.1111/jch.14768. Epub 2024 Jan 12.
7
Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.探讨重金属与糖尿病视网膜病变的关系：一种机器学习建模方法。
Sci Rep. 2024 Jun 6;14(1):13049. doi: 10.1038/s41598-024-63916-w.
8
Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method.使用 SHAP（Shapley Additive exPlanations）方法创建可解释的机器学习模型，将系统性炎症指数、性激素和膳食抗氧化剂联系起来，以识别痛风。
Front Immunol. 2024 May 1;15:1367340. doi: 10.3389/fimmu.2024.1367340. eCollection 2024.
9
Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.通过可解释的机器学习模型揭示肌肉质量下降的环境化学基础。
Am J Clin Nutr. 2024 Aug;120(2):407-418. doi: 10.1016/j.ajcnut.2024.05.022. Epub 2024 May 31.
10
Effects of heavy metal exposure on hypertension: A machine learning modeling approach.重金属暴露对高血压的影响：一种机器学习建模方法。
Chemosphere. 2023 Oct;337:139435. doi: 10.1016/j.chemosphere.2023.139435. Epub 2023 Jul 6.
Zotero 插件