Detection of rare medical events in electronic health records using machine learning: Current practices and suggestions – A scoping review
We identified a need for greater attention in the current medical anomaly detection literature for reporting details on pre-processing, handling of missing data, and the use of performance metrics. With the increasing number of anomaly detection studies based on routine healthcare data stored in EHRs, more focus is needed on implementation and reporting practices to ensure relevance and reproducibility of future studies in this field.
Authors
- Bennett Kleinberg (ORCID: https://orcid.org/0000-0003-1658-9086)
- Esther de Vries (ORCID: https://orcid.org/0000-0003-4311-3550)
- Katrijn Van Deun (ORCID: https://orcid.org/0000-0002-2271-793X)
- Biniyam Y. Gebeyehu
Institutions
- Tilburg University (NL)
- Elisabeth-TweeSteden Ziekenhuis (NL)
- Jeroen Bosch Ziekenhuis (NL)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-03-16
- DOI
- https://doi.org/10.1371/journal.pone.0332963
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00