Choi Insu, Kim Jihye, Kim Woo Chang
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin, South Korea.
Front Nutr. 2022 Mar 9;9:765794. doi: 10.3389/fnut.2022.765794. eCollection 2022.
In this study, we observed the changes in dietary patterns among Korean adults in the previous decade. We evaluated dietary intake using 24-h recall data from the fourth (2007-2009) and seventh (2016-2018) Korea National Health and Nutrition Examination Survey. Machine learning-based methodologies were used to extract these dietary patterns. Particularly, we observed three dietary patterns from each survey similar to the traditional and Western dietary patterns in 2007-2009 and 2016-2018, respectively. Our results reveal a considerable increase in the number of Western dietary patterns compared with the previous decade. Thus, our study contributes to the use of novel methods using natural language processing (NLP) techniques for dietary pattern extraction to obtain more useful dietary information, unlike the traditional methodology.
在本研究中，我们观察了韩国成年人在过去十年间的饮食模式变化。我们使用第四次（2007 - 2009年）和第七次（2016 - 2018年）韩国国家健康与营养检查调查的24小时回忆数据来评估饮食摄入量。基于机器学习的方法被用于提取这些饮食模式。具体而言，我们在每次调查中分别观察到三种类似于2007 - 2009年和2016 - 2018年传统饮食模式和西方饮食模式的饮食模式。我们的结果显示，与前十年相比，西方饮食模式的数量有了显著增加。因此，与传统方法不同，我们的研究有助于使用自然语言处理（NLP）技术的新方法来提取饮食模式，以获得更有用的饮食信息。
