TPLUFIB-WEB: A fuzzy linguistic Web system to help in the treatment of low back pain problems

Esteban, Bernabé; Tejeda-Lorente, Álvaro; Porcel, Carlos; Arroyo, Manolo; Herrera‐Viedma, Enrique

doi:10.1016/j.knosys.2014.03.004

Cited by 22 publications

(17 citation statements)

References 36 publications

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“…Several extracted articles outlined a hybrid RS that combined content- based and collaborative filtering techniques; a hybrid system has the potential to enhance efficiency, as only the top-ranked most applicable items as well as similar items that are matched on metadata of items preferred in the past are recommended to the target user. 19,26,27,32,36,42,43,45 For example, in the study by Esteban and colleagues, 36 the RS incorporated information from databases of exercise recommendations and patient pathology, as well as users’ ratings on the recommended exercises they completed, and was thus able to generate a limited but tailored number of exercises for the prevention of lower back pain problems. Similarly, in the study by Narducci et al., 43 the hybrid RS recommended only the top-ranked physicians and health facilities for a patient by integrating information from his or her personal health record and ratings of health facilities or doctors consulted by other patients with a similar health status.…”

Section: Resultsmentioning

confidence: 99%

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

et al. 2019

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ObjectiveTailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential of recommender systems to support and enhance computer-tailored digital health interventions. The aim is threefold, to explore: (1) how recommender systems provide health recommendations, (2) to what extent recommender systems incorporate theoretical models and (3) how the use of recommender systems may enhance the usage of computer-tailored interventions.MethodsA scoping review was conducted, using MEDLINE and ScienceDirect, to identify health recommender systems reported in studies between January 2007 and December 2017. Information was subsequently extracted to understand the potential benefits of recommender systems for computer-tailored digital health programs. Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form.ResultsA total of 26 articles were included for data extraction. General characteristics were reported, with eight studies reporting hybrid filtering. A description of how each recommender system provides a recommendation is described; the majority of recommender systems used messages as recommendation. We identified the potential effects of recommender systems on efficiency, effectiveness, trustworthiness and enjoyment of the digital health program.ConclusionsIncorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience. This study illustrates how recommender systems, especially hybrid programs, may have the potential to bring tailored digital health forward.

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Section: Resultsmentioning

confidence: 99%

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

et al. 2019

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“…To assist voters to make decisions in the e-election process, a recommender system was proposed [75], which uses fuzzy clustering methods and provides information about candidates close to voters' preferences. To provide personalized exercises to patients with low back pain problems and to offer recommendations for their prevention, a recommender system called TPLUFIB-WEB was presented in [77]. The system can be used in any place and at any time, yielding savings in travel and staffing costs.…”

Section: G2c Service Recommendationmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,357

546

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A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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“…For example, by using medication data and further patient data, recommender systems could be used to suggest medication that has less side effects (Zhang et al, 2016). Health recommender systems could suggest therapies that better match patients' dispositions and adherence behaviors (Hidalgo et al, 2014;Esteban et al, 2014). By suggesting items that have been satisfactory for patients with a similar health status or disease history, first access to personalized medicine could be achieved.…”

Section: Introductionmentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

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Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n = 521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.

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TPLUFIB-WEB: A fuzzy linguistic Web system to help in the treatment of low back pain problems

Cited by 22 publications

References 36 publications

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

How recommender systems could support and enhance computer-tailored digital health programs: A scoping review

Recommender system application developments: A survey

The users’ perspective on the privacy-utility trade-offs in health recommender systems

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