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 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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Number of pages | 24 |
| Publisher | Springer Verlag |
| Publication date | 2016 |
| Pages | 391-414 |
| DOIs | |
| Publication status | Published - 2016 |
Funding
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. 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.
