Health Recommender System design in the context of CAREGIVERSPRO-MMD Project

Oliva-Felipe, Luis; Barrué, Cristian; Cortés, Àtia; Wolverson, Emma; Antomarini, Marco; Landrin, I; Votis, Konstantinos; Paliokas, Ioannis; Cortés, Ulises

doi:10.1145/3197768.3201558

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Basing recommendations on others with similar socio-economic or ethnic/racial minoritized backgrounds may make HRS even more effective. Further, social media network data can be used, as shown by Oliva-Felipe and colleagues, who developed a HRS with educational contents tailored to their specific profile of dementia caregivers [19]. HRS could also leverage peer-endorsements, allowing users to see recommendations or reviews from their social connections or members of a community with similar health goals [16].…”

Section: Interpersonal Levelmentioning

confidence: 99%

Designing Health Recommender Systems with a Health Equity Lens (Preprint)

Figueroa,

Torkamaan,

Bhattacharjee

et al. 2024

Preprint

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UNSTRUCTURED Health Recommender Systems (HRS), which use Artificial Intelligence (AI), have made great strides for human-centered care and prevention by providing personalized health advice on personal digital devices. HRS have demonstrated a unique role in the digital health field because they can offer relevant recommendations, not only based on what users themselves prefer and may be receptive to, but also using data about wider spheres of influence over human behavior, from peers, families, communities, and societies. Using the socioecological model, we identify how HRS could play a unique role in decreasing health inequities by targeting the interconnectedness of individuals and their environments. We then discuss the challenges and future research priorities. Despite the potential for targeting more complex systemic challenges in obtaining good health, current HRS are still focused on individual health behaviors, do not integrate lived experiences of users, and have had limited reach and effectiveness for individuals from low socioeconomic status (SES) and racial/ethnic minoritized backgrounds. In this perspective, we argue that a new design paradigm is necessary, in which future HRS focus on incorporating structural barriers to good health in addition to user preferences, and are designed from decolonial perspectives. If these steps are taken, HRS could play a crucial role in decreasing health inequities.

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Section: Interpersonal Levelmentioning

confidence: 99%

Designing Health Recommender Systems with a Health Equity Lens (Preprint)

Figueroa,

Torkamaan,

Bhattacharjee

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, their application can be found in various approaches such as telehealth [37], chatbots [36], or mobile applications [41]. These approaches are also used to address different areas of healthcare, such as diets [38], drug treatment [39], dementia [40], support for the elderly [41], or chronic diseases [42]. The described recommender systems mostly use a hybrid collaborative filtering technique to overcome the cold start problem [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Towards a Reference Architecture for Female-Sensitive Drug Management

Mateja

Bartels²,

Oberste³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Due to various biological factors, males and females differ in their response to drug treatment. However, there is still a lack of knowledge of the effects resulting from sex-differences in the medical field, primarily due to females' underrepresentation in clinical studies. Considering severe diseases related to the cardiovascular system, which are likely to be perilous, counteracting this lack and emphasizing the need for sex-dependent drug treatment is of high importance. Thus, this research-in-progress paper aims to strengthen the female perspective in drug management by proposing design considerations on IS regarding recommender systems in healthcare to reinforce shared decision-making and person-centered care. The resulting artifact presented will be a reference architecture with a mobile application as the interface to patients and healthcare professionals as well as a data-driven backend to collect and process data on sex specificity in the medical treatment of cardiovascular diseases (CVD).

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“…There are some researches done on recommendation techniques for other types of items or services such as for recommending a recommendation related to the health [9] and nutrition [10]- [14]. In [9], the main focus of this recommender system is to build a digital platform focusing on people with dementia problem which most of this problem having by elderly. This recommender system provides interventions to caregivers and people living with dementia.…”

Section: A Related Workmentioning

confidence: 99%

A Collaborative Filtering Recommender System Model for Recommending Intervention to Improve Elderly Well-being

Azmi¹,

Abdullah²,

Akmar³

2019

IJACSA

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In improving elderly well-being nowadays, people at home or health care centre are mostly focusing on guarding and monitoring the elderly using tools, such as CCTV, robots, and other appliances that require a great deal of cost and neat fixtures to prevent damage. Elderly observations using the recommender system are found to be implemented, but only focusing on one aspect such as nutrition and health. However, it is important to give interventions to an elderly by concentrating more on the multiple aspects of successful ageing such as social, environment, health, physical, mental and other so that it can help the elderly people in achieving successful ageing as well as improving their well-being. In this paper, two recommender system models are proposed to recommend interventions for improving elderly well-being in the multiple aspects of successful ageing. These models using a Collaborative Filtering (CF) technique to recommend interventions to an elderly based on the interventions given to other elderly who have similar conditions with the user. The process of recommending interventions involves the generation of user profiles presenting the elderly conditions in multiple aspects of successful ageing. It also applying the k-Nearest Neighbor (kNN) method to find users with similar conditions and recommending interventions based on the interventions given to the similar user. The experiment is conducted to determine the performance of the proposed Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) compared to the Basic Search (BS). The results of the experiment showed that Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) outperformed Basic Search (BS) in terms of precision, recall and F1 measure. This result showed that the proposed models are efficient to recommend interventions using elderly profiles based on many aspects of successful ageing.

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Health Recommender System design in the context of CAREGIVERSPRO-MMD Project

Cited by 9 publications

References 21 publications

Designing Health Recommender Systems with a Health Equity Lens (Preprint)

Designing Health Recommender Systems with a Health Equity Lens (Preprint)

Towards a Reference Architecture for Female-Sensitive Drug Management

A Collaborative Filtering Recommender System Model for Recommending Intervention to Improve Elderly Well-being

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