The United Nations states “End hunger, achieve food security and improved nutrition and promote sustainable agriculture" as one of its sustainable development goals by the target date of 2030. To achieve these goals, global food and agriculture systems will require profound changes, in which big data and AI technologies can play significant roles.
In the past decade, a huge amount of work has been done in biomedical predictive modelling. While there are extensive resources available for the biomedical domain, the food and nutrition domains are relatively low AI-resourced. There are few food named-entity recognition systems for the extraction of food and nutrient concepts. In addition, the available food and nutrition ontologies are developed for a very narrow use cases, and there are no links between these ontologies that can be used for food and nutrition data management.
The focus on this track is to provide an overview of AI methods that have already existed for food and nutrition data, together with methods for linking biomedical research data with food and nutrition data as well as on methods that address key challenges arising in application areas relevant to personalized nutrition and medicine.