Dietary Pattern Extraction Using Natural Language Processing Techniques - Choi, Insu; Kim, Jihye; Kim, Woo Chang (Frontiers in Nutrition, 2022)
This study aimed to conduct to observe the changes in dietary patterns among Korean adults over the last 10 years. Dietary intake was assessed by 24-hour recall data from the fourth (2007-2009) and the seventh (2016-2018) Korea National Health and Nutrition Examination Survey. The machine learning-based methodologies were used to extract dietary patterns. We observed three dietary patterns from each survey, which are similar to traditional dietary patterns in 2007-2009 and more westernized dietary patterns in 2016-2018. This study contributes the novel and original methodology using natural language processing (NLP) techniques to extract dietary patterns to obtain useful dietary information, unlike traditional dietary pattern extraction. This study shows that there has been an increase in the number of health-critical western patterns compared to 10 years ago, which can be predicted to be highly related to the increase in chronic diseases in Koreans.