A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition

Lu, Qin; Xu, Xiaowei; Li, Jiao

doi:10.1007/978-3-319-94301-5_31

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…These systems rely on the analysis of personal historical data, among other datasets, to identify suitable products, courses, or services [23]. Recommendation systems can reduce the time required to explore the needed data [23,39]. In healthcare services, many healthcare providers have applied personalized recommendation systems to understand patients' health conditions and suggest personalized healthcare services to each person [41][42][43][44].…”

Section: Personalized Recommendations In Healthcarementioning

confidence: 99%

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Suwan

Temdee

Prasad

2022

JMM

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Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users’ preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group.

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Section: Personalized Recommendations In Healthcarementioning

confidence: 99%

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Suwan

Temdee

Prasad

2022

JMM

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“…More similar works to our approach are [42][43][44][45][46]. In [42], the authors combine two health information recommendation services-a collaborative filtering and a physiological indicator-based recommender-providing to the users useful health information.…”

Section: Recommendations In the Health Domainmentioning

confidence: 99%

“…From a different perspective, the authors of [45] decouples users and items, considering properties related to users and items, based on which a collaborative filtering model is defined. On the other hand, the authors of [46] focus on helping help health providers acquire new knowledge in real-time. However, even in those works, notions like group recommendations and fairness are not considered, nor interesting profile dimensions like the educational level, the health literacy, and the psychoemotional status.…”

Section: Recommendations In the Health Domainmentioning

confidence: 99%

Multidimensional Group Recommendations in the Health Domain

2020

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Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents.

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“…There are also reported efforts on the use of recommendation systems for wellness therapy [31]. Many of the recommender systems for healthcare are deployed towards addressing specific challenges facing healthcare professionals such as reduction of errors in daily clinical consultation [50], Nutrition recommendation for the care of the elderly [19], helping patients living with chronic diseases to monitor and control their cases [25], providing patient eccentric services [25], providing personalized treatment recommendations [27], while others are disease-specific [45], helping providers acquire required knowledge in their profession real-time [39], and personalized food recommendations [43] among others. These efforts already made in the application of recommendation system to the field of healthcare in general and in helping to deliver care for the older one and managing chronic illnesses, indicate their potentiality in Connected Health.…”

Section: B Recommendation Systems In Healthcarementioning

confidence: 99%

Integration of Recommendation Systems Into Connected Health for Effective Management of Chronic Diseases

Afolabi

Toivanen

2019

IEEE Access

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An important trend in meeting the needs of modern caregiving is providing a well-rounded care delivery. In doing this, access to data of care receiver is important. This, however, has been shown to be possible with connected health with the aid of modern technology. In this paper, we are presenting a design that will expand on the opportunities for better data accessibility and use, by integrating the recommendation system into connected health. In order to ensure a design that meets the needs of care receivers, we conducted two independent surveys with a view to gathering requirements for the design. The result has shown the relevance of timeliness in caregiving along with what the care receivers are interested in such as what their data say about their conditions, the immediate and future expectation of their conditions, and collaborative efforts in managing their conditions. The requirements gathered were used in the design and implementation of the mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). The benefits of this solution have been real-time recommendations' availability, cost saving, adequate care, and access to health data and information for better care decision-making.INDEX TERMS Recommender systems, connected health real-time recommendations, ReTReSCAP, ReSCAP, healthcare.

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A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition

Cited by 5 publications

References 10 publications

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Multidimensional Group Recommendations in the Health Domain

Integration of Recommendation Systems Into Connected Health for Effective Management of Chronic Diseases

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