Recommender systems for health informatics: State-of-the-art and future perspectives

André Calero Valdez*, Martina Ziefle, Katrien Verbert, Alexander Felfernig, Andreas Holzinger

*Corresponding author for this work
83 Citations (Scopus)


Recommender systems are a classical example for machine learning applications, however, they have not yet been used extensively in health informatics and medical scenarios. We argue that this is due to the specifics of benchmarking criteria in medical scenarios and the multitude of drastically differing end-user groups and the enormous contextcomplexity of the medical domain. Here both risk perceptions towards data security and privacy as well as trust in safe technical systems play a central and specific role, particularly in the clinical context. These aspects dominate acceptance of such systems. By using a Doctor-in-the- Loop approach some of these difficulties could be mitigated by combining both human expertise with computer efficiency. We provide a three-part research framework to access health recommender systems, suggesting to incorporate domain understanding, evaluation and specific methodology into the development process.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages24
PublisherSpringer Verlag
Publication date2016
Publication statusPublished - 2016

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