Explainable unsupervised anomaly detection for healthcare insurance data
The proposed workflow is able to detect known care providers with atypical behaviour and helps expert investigators in making informed decisions concerning possible fraud or overconsumption in the health insurance field.
Authors
- Hannes De Meulemeester (ORCID: https://orcid.org/0000-0002-5938-2387)
- Frank De Smet (ORCID: https://orcid.org/0000-0002-0656-5921)
- Johanna Dorst (ORCID: https://orcid.org/0000-0002-2028-6291)
- Bart De Moor (ORCID: https://orcid.org/0000-0001-5836-2037)
- Elise Derroitte
Institutions
- KU Leuven (BE)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1186/s12911-024-02823-6
- Citations
- 9
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 22.33
Funders
- KU Leuven
- Kom op tegen Kanker
- Vlaamse regering
- HORIZON EUROPE European Research Council