About this Research Topic
In the past decade, a considerable amount of work has been done in biomedical predictive modeling. This would not be possible without the existence of diverse biomedical vocabularies and standards, which play a crucial role in understanding biomedical information, together with a large amount of biomedical data collected ( e.g., drug, diseases, and other treatments) from numerous sources. While there are extensive resources available for the biomedical domain, the food and nutrition domain is still relatively low resourced. There are only a few food named-entity recognition systems for the extraction of food and nutrient concepts from unstructured data. Further, the available ontologies considering food and nutrition are designed for very specific applications and narrow use cases, and there are no links between these ontologies that can be used for food and nutrition data management.
The proposed Research Topic, which is inspired by the "AI & Nutrition" track organized in the context of the AMLD 2020 conference, aims to focus on methods for linking and exploring relations between food and nutrition data with health and environmental sustainability, as well as on advanced methods that address key challenges arising in application areas relevant to food and nutrition.
Topics of interest include algorithms, methods, and systems related to food and nutrition:
- Information retrieval and extraction in efforts to build food ingredient databases;
- Data normalization, ontologies, and ontology design in efforts to record individual eating patterns with great detail and link eating to important locational, temporal, and social factors, including unstructured (social media, text, images etc.) and structured data resources;
- Predict relationships between food and nutrition and health behaviors, linking this to health and environment outcomes;
- Recommender systems in efforts to build personalized nutrition systems and drive food choices;
- NLP frameworks in efforts to inform community interventions and population health and environment policies that affect access to and consumption of food;
- Digital tracking tools, wearable devices, and other sensors in efforts to record, represent, and analyze quantified-self data, and link food consumption to health and environmental sustainability.
The proposed Research Topic, which is inspired by the "AI & Nutrition" track organized in the context of the AMLD 2020 conference, aims to focus on methods for linking and exploring relations between food and nutrition data with health and environmental sustainability, as well as on advanced methods that address key challenges arising in application areas relevant to food and nutrition.
Topics of interest include algorithms, methods, and systems related to food and nutrition:
- Information retrieval and extraction in efforts to build food ingredient databases;
- Data normalization, ontologies, and ontology design in efforts to record individual eating patterns with great detail and link eating to important locational, temporal, and social factors, including unstructured (social media, text, images etc.) and structured data resources;
- Predict relationships between food and nutrition and health behaviors, linking this to health and environment outcomes;
- Recommender systems in efforts to build personalized nutrition systems and drive food choices;
- NLP frameworks in efforts to inform community interventions and population health and environment policies that affect access to and consumption of food;
- Digital tracking tools, wearable devices, and other sensors in efforts to record, represent, and analyze quantified-self data, and link food consumption to health and environmental sustainability.
Keywords: artificial intelligence, food consumption, digital track tools, nutrition, recognition systems, unstructured data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
