Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Thi Ngoc Trang Tran, Alexander Felfernig, Christoph Trattner & Andreas Holzinger

Publisher name:  Springer

DOI:  10.1007/s10844-020-00633-6

Volume Information:  volume 57, pages:171–201 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s10844-020-00633-6