Exploring synergies between digital tv recommender systems and electronic health records

López‐Nores, Martín; Blanco‐Fernández, Yolanda; Pazos‐Arias, José J.; García‐Duque, Jorge

doi:10.1145/1809777.1809804

Cited by 5 publications

(5 citation statements)

References 31 publications

(38 reference statements)

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“…López-Nores et al presented a number of enhancements that enable Digital Television (DTV) Recommender Systems to exploit the synergies between healthcare and areas like entertainment, electronic commerce, and education [16]. The study raised privacy concerns regarding the storing of sensitive patients data in a remote sever noting that nothing stops server owners from exploiting this sensitive information.…”

Section: Literature Survey On Healthcare Recommender Systems (Hrs)mentioning

confidence: 99%

A Survey of Context-Aware Healthcare Recommender Systems

2017

IJSR

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Abstract:The advent of social web has transformed users from sheer consumers to ardent producers of information. The rapid growth coupled with the reliance on the internet for information ranging from e-commerce, e-government and social networks has led to infobesity. Recommender Systems have become essential tool to aid users to overcome the problem of information overload and provide personalized relevant suggestions. The use of Recommender Systems have been successful in both industry and academia. Recommender systems have also found their way into the Healthcare domain with varied applications.Despite the growing practical demand, a few challenges are hindering the adoption ofRecommender Systems in the Healthcare.The purpose of this study is to understand the trend of Recommender Systems applications inHealthcareby examining the published literature, and to provide practitioners and researchers with insight and future direction.We conducted a focused literature review to identify research papers on Recommender Systems in the Healthcare in the last ten years. The key findings of this survey is that the application of Recommender Systems inHealthcare is evolving. The incorporation of contextual information is limited although it has been suggested as a key ingredient to improving the quality of the recommendations and the accuracy of the predications.It is important to point out that aside from the common filtering problems of sparsity, cold start and scalability, a socio-technical issue of privacy, security and trust is emerging.Collaborative Filtering technique is predominantly in use in Healthcare Recommender Systems. Incorporation of contextual information is limited.Privacy, security and trust concerns may hinder the widespread use and acceptance of the Healthcare Recommender Systems.

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Section: Literature Survey On Healthcare Recommender Systems (Hrs)mentioning

confidence: 99%

A Survey of Context-Aware Healthcare Recommender Systems

2017

IJSR

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“…Cornejo et al [23] combined a lightweight face book client with social exer games on an interactive display to encourage social encounters of seniors with family members. López-Nores et al [24] explored options to incorporate health-related recommendations into semantically matching TV programs.…”

Section: Related Workmentioning

confidence: 99%

CARE - Extending a Digital Picture Frame with a Recommender Mode to Enhance Well-Being of Elderly People

Rist

Seiderer

Hammer

et al. 2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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This contribution addresses the development of technology for the elderly with the aim to improve their general well-being. We report work on a prototype system named CARE that combines the functionality of a digital image frame with an active recommender mode. The purpose of the recommender is to provide the user with context-specific recommendations. Recommendations are chosen on the basis of data acquired by sensors embedded in the user's environment, and a well-being model to carefully decide on at which point in time what kind of activity is most suitable to suggest. We describe conception and implementation of a first CARE prototype that we used for in-situ testing in a senior's home, as well as for gathering feedback on system usage and user appreciation. The paper concludes with some lessons learned from analyzing log file data of the trial period and from conducting a post-trial interview.

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“…Different stationary and mobile devices have been investigated to convey health and lifestyle related information to users including digital picture frames [15], TV sets [9], smart bracelets [4] or robotic companions [19]. Furthermore, various metaphers have been explored to convey health-related information to users in persuasive manner.…”

Section: Health and Lifestyle Recommender Systemsmentioning

confidence: 99%

Design of a lifestyle recommender system for the elderly

Hammer

Seiderer

André

et al. 2015

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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As overaging is becoming a main societal challenge, the development of AAL (Ambient Assisted Living) systems has become the centre of many research projects the last years. Our own work is targeted towards the development of an AAL system-called CARE-that provides assistance in form of recommendations helping its users overcome typical difficulties of everyday life, and contributes positively to their well-being. To inform the design of the envisioned CARE system we recruited two peer groups of potential users, a group of 20 Greek seniors, and a group of 27 German seniors, and conducted structured interviews which were focused on the seniors' lifestyle , medical needs, attitude towards AAL technologies, and, more specifically, on desired functions and system configurations of a recommendationgiving CARE system. We discuss outcomes of the conducted interviews and sketch a first CARE prototype which appears as an augmented digital picture frame that interleaves the display of photos with recommendations and interventions to improve the seniors' lifestyle and well-being.

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Exploring synergies between digital tv recommender systems and electronic health records

Cited by 5 publications

References 31 publications

A Survey of Context-Aware Healthcare Recommender Systems

A Survey of Context-Aware Healthcare Recommender Systems

CARE - Extending a Digital Picture Frame with a Recommender Mode to Enhance Well-Being of Elderly People

Design of a lifestyle recommender system for the elderly

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