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

Andre Calero-Valdez, Martina Ziefle, Katrien Verbert, Alexander Felfernig, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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
Original languageEnglish
Title of host publicationMachine Learning for Health Informatics
Place of PublicationCham
PublisherSpringer
Pages1-24
Number of pages24
ISBN (Print)978-3-319-50477-3
DOIs
Publication statusPublished - 31 Dec 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume 9605

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Fields of Expertise

  • Information, Communication & Computing
  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Application
  • Review

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