PREFer: A prescription-based food recommender system

Bianchini, Devis; Antonellis, Valeria De; Franceschi, Nicola De; Melchiori, Michele

doi:10.1016/j.csi.2016.10.010

Cited by 68 publications

(38 citation statements)

References 22 publications

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“…Bianchini et al [ 27 ] proposed the PREFer food recommender system, apt to provide users with personalized and healthy menus, taking into account both user's short/long-term preferences and medical prescriptions. Prescriptions classify the ideal user ' s nutrition behaviour from the health point of view, with constraints imposed by the specific user's phenotype but lack Diabetes questions and answers chatbot to provide education on diabetes as well as medicine reminder notification on smartphones and food recognition capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Diabetes Management System Using Machine Learning

Sowah

Bampoe-Addo

Armoo

et al. 2020

International Journal of Telemedicine and Applications

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This paper describes the design and implementation of a software system to improve the management of diabetes using a machine learning approach and to demonstrate and evaluate its effectiveness in controlling diabetes. The proposed approach for this management system handles the various factors that affect the health of people with diabetes by combining multiple artificial intelligence algorithms. The proposed framework factors the diabetes management problem into subgoals: building a Tensorflow neural network model for food classification; thus, it allows users to upload an image to determine if a meal is recommended for consumption; implementing K-Nearest Neighbour (KNN) algorithm to recommend meals; using cognitive sciences to build a diabetes question and answer chatbot; tracking user activity, user geolocation, and generating pdfs of logged blood sugar readings. The food recognition model was evaluated with cross-entropy metrics that support validation using Neural networks with a backpropagation algorithm. The model learned features of the images fed from local Ghanaian dishes with specific nutritional value and essence in managing diabetics and provided accurate image classification with given labels and corresponding accuracy. The model achieved specified goals by predicting with high accuracy, labels of new images. The food recognition and classification model achieved over 95% accuracy levels for specific calorie intakes. The performance of the meal recommender model and question and answer chatbot was tested with a designed cross-platform user-friendly interface using Cordova and Ionic Frameworks for software development for both mobile and web applications. The system recommended meals to meet the calorific needs of users successfully using KNN (with k=5) and answered questions asked in a human-like way. The implemented system would solve the problem of managing activity, dieting recommendations, and medication notification of diabetics.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Diabetes Management System Using Machine Learning

Sowah

Bampoe-Addo

Armoo

et al. 2020

International Journal of Telemedicine and Applications

View full text Add to dashboard Cite

show abstract

“…These research works have been focused on the adaptive delivery of healthy diet plans to improve the quality of life of both healthy subjects and patients with diet-related chronic diseases [2], [16], [33]. With this purpose in mind, they have used flow charts supported by user answers to dynamic medical questionnaires [2], social semantic mobile framework to generate healthcare-related recommendation [33], as well as the use of ontologies for managing recipes, menus, and medical prescriptions [7]. Further key research works focused on extensive nutritional information modeling were developed by Espin et al [16] focusing on helping elderly users to draw up their own healthy diet plans, and by Cioara et al [13], where dietary knowledge is defined by nutritionists and encoded as a nutrition care process ontology.…”

Section: B Related Work In Food Recommendationmentioning

confidence: 99%

A Food Recommender System Considering Nutritional Information and User Preferences

2019

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The World Health Organization identifies the overall increasing of noncommunicable diseases as a major issue, such as premature heart diseases, diabetes, and cancer. Unhealthy diets have been identified as the important causing factor of such diseases. In this context, personalized nutrition emerges as a new research field for providing tailored food intake advices to individuals according to their physical, physiological data, and further personal information. Specifically, in the last few years, several types of research have proposed computational models for personalized food recommendation using nutritional knowledge and user data. This paper presents a general framework for daily meal plan recommendations, incorporating as main feature the simultaneous management of nutritional-aware and preference-aware information, in contrast to the previous works which lack this global viewpoint. The proposal incorporates a pre-filtering stage that uses AHPSort as multi-criteria decision analysis tool for filtering out foods which are not appropriate to the current user characteristics. Furthermore, it incorporates an optimization-based stage for generating a daily meal plan whose goal is the recommendation of food highly preferred by the user, not consumed recently, and satisfying his/her daily nutritional requirements. A case study is developed for testing the performance of the recommender system.INDEX TERMS Daily meal plan recommendation, user preferences, nutritional information, multi-criteria decision making, recommender systems.

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“…Bianchini, De Antonellis, De Franceschi, and Melchiori () proposed the Prescriptions for REcommending Food (PREFer) system, which recommends personalized healthy menus considering a user's short‐/long‐term preferences and medical prescriptions. The researchers considered the recommendation of menus in three steps.…”

Section: Key Phrs Applications and Case Studies In Literaturementioning

confidence: 99%

A survey on semanticized and personalized health recommender systems

Ertuğrul

Elçi

2019

Expert Systems

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Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.

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PREFer: A prescription-based food recommender system

Cited by 68 publications

References 22 publications

Design and Development of Diabetes Management System Using Machine Learning

Design and Development of Diabetes Management System Using Machine Learning

A Food Recommender System Considering Nutritional Information and User Preferences

A survey on semanticized and personalized health recommender systems

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