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Machine Learning for Health Informatics pp 391–414Cite as

Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

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 context-complexity 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.

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Notes

  1. 1.

    Netflix is an online movie provider.

  2. 2.

    http://www.recsyswiki.com/wiki/Category:Dataset.

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Acknowledgments

The authors thank the German Research Council DFG for the friendly support of the research in the excellence cluster “Integrative Production Technology in High Wage Countries”, and the anonymous reviewers for their constructive comments.

Part of the work of Katrien Verbert has been supported by the Research Foundation Flanders (FWO), grant agreement no. G0C9515N, and the KU Leuven Research Council, grant agreement no. STG/14/019.

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Authors and Affiliations

  1. Human-Computer Interaction Center, RWTH-Aachen University, Campus Boulevard 57, 52074, Aachen, Germany

    André Calero Valdez & Martina Ziefle

  2. KU Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium

    Katrien Verbert

  3. Institute for Software Technology, Graz University of Technology, 8010, Graz, Austria

    Alexander Felfernig

  4. Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics & Documentation, Medical University Graz, 8036, Graz, Austria

    André Calero Valdez & Andreas Holzinger

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  1. André Calero Valdez
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Correspondence to André Calero Valdez .

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  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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Calero Valdez, A., Ziefle, M., Verbert, K., Felfernig, A., Holzinger, A. (2016). Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_20

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