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.

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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
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article

Detection of rare medical events in electronic health records using machine learning: Current practices and suggestions – A scoping review

Bennett Kleinberg, Esther de Vries, Katrijn Van Deun, Biniyam Y. Gebeyehu
PLoS ONE
Anomaly Detection Techniques and Applications
article

Detection of rare medical events in electronic health records using machine learning: Current practices and suggestions – A scoping review

Bennett Kleinberg, Esther de Vries, Katrijn Van Deun, Biniyam Y. Gebeyehu
article en

Abstract

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.

PLoS ONEVol. 21(3)
Tilburg University (NL), Elisabeth-TweeSteden Ziekenhuis (NL), Jeroen Bosch Ziekenhuis (NL)
Reduced inequalities
Openalex Percentile: Top 24%
Anomaly Detection Techniques and Applications
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