Recommender systems in the healthcare domain: state-of-the-art and research issues
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Recommender systems in the healthcare domain: state-of-the-art and research issues Thi Ngoc Trang Tran1 Andreas Holzinger3
· Alexander Felfernig1 · Christoph Trattner2 ·
Received: 4 June 2020 / Revised: 18 November 2020 / Accepted: 19 November 2020 / © The Author(s) 2020
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 healthrelated 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. Keywords Health recommender systems · Food recommendation · Drug recommendation · Health status prediction · Healthcare service recommendation · Healthcare professionals recommendation
1 Introduction In the past decades, 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. Such digital information is often scattered across different sites, which hinders users from finding useful information for their well-being improvement. Besides, Thi Ngoc Trang Tran
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Extended author information available on the last page of the article.
Journal of Intelligent Information Systems
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. Recommender systems have been integrated into online retailers, streaming services, and social networks to facilitate users’ item selection process (Felfernig and Gula 2006; Tran et al. 2018). Recently, these systems have been widely applied to the healthcare domain (so-called Health Recommender Systems - HRS) to better support medical suggestions. Different from the precursors in t
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