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| Hauptseite > Publikationsdatenbank > Anomaly Detection in Intensive Care Unit Data: A Comparative Analysis |
| Hauptseite > Publikationsdatenbank > Anomaly Detection in Intensive Care Unit Data: A Comparative Analysis |
| Poster (After Call) | FZJ-2026-02025 |
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2026
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Please use a persistent id in citations: doi:10.5220/0014470400004070
Abstract: We present a comparative analysis of anomaly detection (AD) methods applied to vital sign time series data from intensive care units (ICUs). Our study evaluates five unsupervised deep learning-based and three statistical or rule-based AD algorithms with respect to their ability to identify anomalous data points in a binary anomaly classification setting. We have access to physician-annotated datasets of 44 ICU patients. Of these, 38 data sets were used for training and six for evaluation. In addition, selected deep learning models were retrained on a larger dataset comprising 3,000 patients to assess the effect of training set size. Performance is assessed using the F1 score by comparing algorithm outputs with expert annotations. Among all methods, OmniAnomaly and DeepAnT achieved the highest F1 scores, reaching up to 98.38 % and 78.07 %, respectively.
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