Design and Development of Ontology for AI-Based Software Systems to Manage the Food Intake and Energy Consumption of Obesity, Diabetes and Tube Feeding Patients

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Martinho, D (Autor) Outra; Crista, V (Autor) Outra; Karakaya, Z (Autor) Outra; Gamechi, Z (Autor) Outra; Freitas A (Autor) FMUP; Neves, J (Autor) Outra; Novais, P (Autor) Outra; Marreiros, G (Autor) Outra

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I

Páginas: 29-40

22nd EPIA Conference on Artificial Intelligence (EPIA)

Azores, PORTUGAL, SEP 05-08, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-KWR

DOI: 10.1007/978-3-031-49008-8_3

Abstract (EN): Poor and sedentary lifestyles combined with bad dietary habits have an impact on our health. Nowadays, diet-related diseases have become a major public health issue, threatening the sustainability of healthcare systems, and new strategies to promote better food intake are now being explored. In this context, the use of ontologies has gained importance over the past decade and become more prevalent. By incorporating ontologies in the healthcare domain, artificial intelligence (AI) can be enhanced to better support healthcare systems dealing with chronic diseases, such as obesity and diabetes requiring long-term progress and frequent monitoring. This is especially challenging with current resource inefficiency; however, recent research suggests that incorporating ontology into AI-based technological solutions can improve their accuracy and capabilities. Additionally, recommendation and expert systems benefit from incorporating ontologies for a better knowledge representation and processing to increase success rates. This study outlines the development of an ontology in the context of food intake to manage and monitor patients with obesity, diabetes, and those using tube feeding. A standardized vocabulary for describing food and nutritional information was specified to enable the integration with different healthcare systems and provide personalized dietary recommendations to each user.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12