Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Abstract

A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

LLM- Based Personalized Recommendations in Health

“…This information is then transmitted to the app server (2) to initiate the first Case-Based Reasoning (CBR) decision support cycle and obtain the initial plan of actions. This plan is subsequently delivered to the patients' cell phones (5) and can be accessed by the patients (6). All further interactions occur via the app, which collects personalized data from patients every 10 days (4) and also physical activity data of patients from their wearable devices (3).…”

Knowledge Based Recommender System for Disease Diagnostic and Treatment Using Adaptive Fuzzy-Blocks

“…It applies filtering and ranking techniques based on the relevancy of mapping in the knowledge base. In this context, the patient's health record can be seen as explicit user input, which should be transformed into a patient profile to facilitate the mapping with the knowledge base, representing various diagnosis conditions (items) [10]. Rule-based systems are well-suited for representing the knowledge base due to their capacity to offer transparency in design [11].…”

Untangling the Role of Data Processing in Enhancing Recommender Systems Performance

“…Through a systematic study, we explore the various stages of data treatment, their importance, and their direct correlation with the output quality of recommendations [6]. By emphasizing the necessity and impact of these steps, we can guide a new generation of more efficient, accurate, and usercentric recommender systems [7].…”