Food recommender systems for diabetic patients: a narrative review

Norouzi, Somaye and Nematy, Mohsen and Zabolinezhad, Hedieh and Sistani, Samane and Etminani, Kobra (2017) Food recommender systems for diabetic patients: a narrative review. Reviews in Clinical Medicine, 4 (3). pp. 128-130.

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World Health Organization (WHO) estimates that the number of people with diabetes will grow 114 by 2030. It declares that patients themselves have more responsibility for controlling and the treatment of diabetes by being provided with updated knowledge about the disease and different aspects of available treatments, and diet therapy in particular. In this regard, diet recommendation systems would be helpful. They are techniques and tools which suggest the best diets according to patient�s health situation and preferences. Accordingly, this narrative review studied food recommendation systems and their features by focusing on nutrition and diabetic issues. Literature searches in Google scholar and Pubmed were conducted in February 2015. Records were limited to papers in English language; however, no limitations were applied for the published date. We recognized three common methods for food recommender system: collaborative filtering recommender system (CFRS), knowledge based recommender system (KBRS) and context-aware recommender system (CARS). Also wellness recommender systems are a subfield of food recommender systems, which help users to find and adapt suitable personalized wellness treatments based on their individual needs. Food recommender systems often used artificial intelligence and semantic web techniques. Some used the combination of both techniques.

Item Type: Article
Subjects: WD Nutrition Disease and metabolic diseases
WK Endocrine System
Divisions: Journals > Reviews in Clinical Medicine
Depositing User: RCM RCM
Date Deposited: 05 Sep 2017 15:01
Last Modified: 05 Sep 2017 15:01

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