Abstract
Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.
Highlights
In the past decades, a considerable amount of clinical data representing patients’ health status have been collected
To have a deeper look at recommendation scenarios in the healthcare domain, we searched for references using additional keywords: “food recommendation”, “nutrition recommendation”, “drug recommendation”, “heath status prediction”, “healthcare service recommendation”, “physical activity recommendation”, and “doctor recommendation”
We selected and analyzed 37 studies, which provide detailed discussions on recommendation approaches in the healthcare domain. These studies are summarized in Section 4: eight papers related to food recommendation, 18 papers on drug recommendation, three papers related to health status prediction, four papers on physical activity recommendation, and four papers on healthcare professional recommendation
Summary
A considerable amount of clinical data representing patients’ health status (e.g., medical reports, laboratory results, and disease treatment plans) have been collected This has remarkably increased digital information available for patient-oriented decision making. Journal of Intelligent Information Systems (2021) 57:171–201 more drugs, tests, and treatment recommendations are available for medical staff daily, which triggers difficulties in deciding appropriate remedies for patients (Stark et al 2019; Wiesner and Pfeifer 2014) In this context, recommender systems for medical use should be implemented to bridge these gaps and support both, patients and medical professionals, to make better healthcare-related decisions. These systems are expected to minimize the cost of the healthcare-related decision making process (in terms of time and effort) (Valdez et al 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
