Dietary Pattern Extraction Using Natural Language Processing Techniques

Insu Choi; Jihye Kim; Woo Chang Kim

doi:10.3389/fnut.2022.765794

Dietary Pattern Extraction Using Natural Language Processing Techniques

Front Nutr. 2022 Mar 9:9:765794. doi: 10.3389/fnut.2022.765794. eCollection 2022.

Authors

Insu Choi¹, Jihye Kim², Woo Chang Kim¹

Affiliations

¹ Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
² Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin, South Korea.

Abstract

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.

Keywords: dietary pattern; machine learning; natural language processing (NLP); topic modeling; word embedding.