Dataset Information

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading.

ABSTRACT: Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine reading pipeline was used to identify all articles and abstracts on proteomics, diet, food, and nutrition in humans. The resulting proteomic corpus was further analyzed to produce seven clusters of "thematic" content defined as documents that have similar word content. Examples of publications from several of these clusters were then described in a similar way to a typical descriptive review.

SUBMITTER: Monteiro JP

PROVIDER: S-EPMC9863309 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading.

Monteiro Jacqueline Pontes JP Morine Melissa J MJ Ued Fabio V FV Kaput Jim J

Nutrients 20230105 2

Nutrition affects the early stages of disease development, but the mechanisms remain poorly understood. High-throughput proteomic methods are being used to generate data and information on the effects of nutrients, foods, and diets on health and disease processes. In this report, a novel machine reading pipeline was used to identify all articles and abstracts on proteomics, diet, food, and nutrition in humans. The resulting proteomic corpus was further analyzed to produce seven clusters of "them ...[more]

PMID: 36678141

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Project description:BackgroundUnfortified plant-based diets are devoid of vitamin B12, and supply low intakes of iodine, zinc, selenium, and calcium. To disentangle the complex interplay between nutritional adequacy and nutrient intakes from supplements and foods in plant-based diets, data from a Germany-based cross-sectional study examining the nutritional status of omnivores, lacto-ovo-vegetarians and vegans was re-analyzed. Special emphasis was put on potentially under-consumed nutrients in plant-based diets, including vitamin A and choline.MethodsA novel tool focusing on under-consumed micronutrients was employed to shed a new light on nutrient supply and dietary exposure to critical nutrients in plant-based diets: The Total Nutrient Index (TNI). The TNI extends existing measures of diet quality by considering nutrient intake data from both foods and supplements. The TNI covers calcium, magnesium, potassium, choline and vitamins A, C, D, and E. The TNI was compared between omnivores, vegetarians and vegans, with a special focus on its micronutrient component scores and with regard to dietary supplement contributions.ResultsData from 108 participants was analyzed. The vegan and the omnivorous diet resulted in similar TNI scores (73.70 ± 19.68 and 72.77 ± 17.88), whereas lacto-ovo-vegetarians scored lower (68.50 ± 17.10). The contribution of supplements to the TNI was higher in vegans and omnivores (median contribution: 12.50 (16.80) and 10.81 (18.23) score points, respectively) as compared to lacto-ovo-vegetarians (3.42 (12.50) score points). High micronutrient component score contributions to the TNI were found for vitamin D supplements (all dietary groups), vitamin C supplements (omnivores and vegans) and magnesium supplements (all groups).ConclusionsSupplementation has a profound impact on nutrient supplies in individuals on a plant-based diet. This study reiterates the need to quantitatively assess nutrient intakes from supplements to assess diet quality of plant-based dietary patterns. We posit that defining diet-specific TNI scores is important for a precise evaluation of diet quality, whether in omnivore or in the spectrum of plant-based diets.

Project description:BackgroundSocial media platforms allow individuals to openly gather, communicate, and share information about their interactions with health care services, becoming an essential supplemental means of understanding patient experience.ObjectiveWe aimed to identify common discussion topics related to health care experience from the public's perspective and to determine areas of concern from patients' perspectives that health care providers should act on.MethodsThis study conducted a spatiotemporal analysis of the volume, sentiment, and topic of patient experience-related posts on the Weibo platform developed by Sina Corporation. We applied a supervised machine learning approach including human annotation and machine learning-based models for topic modeling and sentiment analysis of the public discourse. A multiclassifier voting method based on logistic regression, multinomial naïve Bayes, and random forest was used.ResultsA total of 4008 posts were manually classified into patient experience topics. A patient experience theme framework was developed. The accuracy, precision, recall, and F-measure of the method integrating logistic regression, multinomial naïve Bayes, and random forest for patient experience themes were 0.93, 0.95, 0.80, 0.77, and 0.84, respectively, indicating a satisfactory prediction. The sentiment analysis revealed that negative sentiment posts constituted the highest proportion (3319/4008, 82.81%). Twenty patient experience themes were discussed on the social media platform. The majority of the posts described the interpersonal aspects of care (2947/4008, 73.53%); the five most frequently discussed topics were "health care professionals' attitude," "access to care," "communication, information, and education," "technical competence," and "efficacy of treatment."ConclusionsHospital administrators and clinicians should consider the value of social media and pay attention to what patients and their family members are communicating on social media. To increase the utility of these data, a machine learning algorithm can be used for topic modeling. The results of this study highlighted the interpersonal and functional aspects of care, especially the interpersonal aspects, which are often the "moment of truth" during a service encounter in which patients make a critical evaluation of hospital services.

Dataset Information

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading.

Publications

Identifying and Analyzing Topic Clusters in a Nutri-, Food-, and Diet-Proteomic Corpus Using Machine Reading.

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