Dietary Pattern Extraction Using Natural Language Processing Techniques

Choi, Ik-Hyeon; Kim, Jihye; Kim, Woo Chang

doi:10.3389/fnut.2022.765794

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The benchmark model developed from this demographic information focuses on forecasting health outcomes based on demographic factors alone. Compared to the benchmark model, our proposed model extends prior research [ 9 , 10 ] and highlights the continued relevance of KNHANES VII in Korean nutritional and health outcomes.…”

Section: Methodssupporting

confidence: 58%

“…The examination of data from 2016 to 2018 led to the discovery of 835 unique food names. The incorporation of additional preprocessing methods, such as the Modu Corpus, and the exclusion of data from 2007–2009 (KNHANES IV), led to slight deviations in the final list of food names compared with those identified in the study by Choi et al (2022) [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

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An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

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Our study harnesses the power of natural language processing (NLP) to explore the relationship between dietary patterns and metabolic health outcomes among Korean adults using data from the Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII). Using Latent Dirichlet Allocation (LDA) analysis, we identified three distinct dietary patterns: “Traditional and Staple”, “Communal and Festive”, and “Westernized and Convenience-Oriented”. These patterns reflect the diversity of dietary preferences in Korea and reveal the cultural and social dimensions influencing eating habits and their potential implications for public health, particularly concerning obesity and metabolic disorders. Integrating NLP-based indices, including sentiment scores and the identified dietary patterns, into our predictive models significantly enhanced the accuracy of obesity and dyslipidemia predictions. This improvement was consistent across various machine learning techniques—XGBoost, LightGBM, and CatBoost—demonstrating the efficacy of NLP methodologies in refining disease prediction models. Our findings underscore the critical role of dietary patterns as indicators of metabolic diseases. The successful application of NLP techniques offers a novel approach to public health and nutritional epidemiology, providing a deeper understanding of the diet–disease nexus. This study contributes to the evolving field of personalized nutrition and emphasizes the potential of leveraging advanced computational tools to inform targeted nutritional interventions and public health strategies aimed at mitigating the prevalence of metabolic disorders in the Korean population.

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Section: Methodssupporting

confidence: 58%

Section: Methodsmentioning

confidence: 99%

An Explainable Prediction for Dietary-Related Diseases via Language Models

Choi,

Kim,

Kim

2024

Nutrients

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“…Finally, Kim et al [30] combined NLP and Machine Learning to identify brain MRI reports with acute ischemic stroke. In nutrition, Choi, Kim, and Kim attempted to use the NLP preprocessing methodologies to extract dietary patterns in Korea using the National Health and Nutrition Examination Survey [31]. Their results indicate a considerable increase in Western dietary patterns.…”

Section: Natural Language Processingmentioning

confidence: 99%

The cause and effect of the nutrition transition in Nigeria: analysis of the value of indigenous knowledge and traditional foods in Enugu State, Igboland

Chukwu,

Dogbe

2023

J. Ethn. Food

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The objective of this study is twofold: one, to analyze the presence of the nutrition transition in Nigeria through assessment of national food consumption trends, obesity, and noncommunicable diseases (NCDs) data over time, and two, to assess the role that indigenous knowledge and foodways can play in promoting healthy diets in the country through the case study of Enugu State, Igboland. A mixed-methods approach was utilized with quantitative data from the Nigerian Living Standards Survey and Demographic Health Survey to measure changes in obesity, diabetes, hypertension, and food consumption across Nigeria from 2003 to 2018 using Natural Language Processing and Trend Analysis. Primary qualitative data in Enugu State were collected through semi-structured interviews in 2021 with a random sampling technique undertaken to analyze the status of indigenous Igbo foods and traditions to curb the negative effects of the nutrition transition, as well as to document the motivations behind using or abandoning traditional foods in the present generation. The findings of the study depict the nutrition transition in Nigeria in multiple forms. The combined overweight and obese population in the country has experienced a 29% increase in the 15-years study period, while the NCDs hypertension and diabetes have experienced a combined 21% increase. Consumption patterns have shifted, though not always in the ways predicted from the literature, with statistically significant increases displayed in the consumption of Breads and Pastries, Starchy Roots and Tubers, Legumes, Plant and Animal Fats, Seafoods, Milk and Dairy Products, Beverages, Non-alcoholic Drinks, Vegetables, and Red Meats. The primary data analysis displayed that the traditional Igbo foodscape is defined by local, nutritious whole foods. The data depicted significant recent trends toward using ultra-processed seasoning cubes instead of local herbs as spices, with a huge shift from 0% frequency usage identified in the parents’ generation to a 35% frequency in the current generation. Eighty-three percent of the community surveyed stated that they believe that the diets of the younger generation are changing, with the majority stating that the change is for the worse. Participants from the community provided recommendations for curbing the nutrition transition and empowering indigenous foodways through policy, investment, and mindset change. Though consumption patterns are changing and Nigeria’s obesity and NCD rates are increasing, it is still far behind the rate of obesity in countries farther along in the nutrition transition such as its former colonizer Britain. Interviews conducted around Enugu State show how the indigenous food system is centered on fresh, healthy foods, primarily local tubers, legumes, vegetables, and spices. There is extensive knowledge and pride in the indigenous food system, even though there was a consensus that Igbo food culture is currently undergoing immense changes in part due to some of the younger generation’s new tastes in processed foods and convenience.

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A Comprehensive Review of Food Recommendation Systems in the Context of Nutritional Therapy for Diabetes Mellitus

Prabakaran,

Rajan,

Chagalasetty

et al. 2025

CNF

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Abstract:: Food recommendation systems (FRS) have gained prominence for providing personalized dietary recommendations. This paper explores the integration of FRS and nutritional therapy to enhance the management of diabetes mellitus. Nutritional therapy plays a crucial role in controlling blood glucose levels and reducing complications in diabetes. The study reviews and analyzes various food recommendation models in diverse scenarios of diabetic nutritional therapy. By considering specific needs and constraints, the strengths of each model are assessed, leading to the identification of the most suitable model for each scenario. The findings contribute to improving the quality of dietary guidance for individuals with diabetes.

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Dietary Pattern Extraction Using Natural Language Processing Techniques

Cited by 4 publications

References 30 publications

An Explainable Prediction for Dietary-Related Diseases via Language Models

An Explainable Prediction for Dietary-Related Diseases via Language Models

The cause and effect of the nutrition transition in Nigeria: analysis of the value of indigenous knowledge and traditional foods in Enugu State, Igboland

A Comprehensive Review of Food Recommendation Systems in the Context of Nutritional Therapy for Diabetes Mellitus

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