AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers Article Swipe
Gregor Donabauer
,
Anca Rath
,
Aila Caplunik-Pratsch
,
Anja Eichner
,
Jürgen Fritsch
,
Martin Kieninger
,
Susanne Gaube
,
Wulf Schneider‐Brachert
,
Udo Kruschwitz
,
Bärbel Kieninger
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000821
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pdig.0000821
The isolation of affected patients and intensified infection control measures are used to prevent nosocomial transmission of vancomycin-resistant enterococci (VRE), but early detection of VRE carriers is needed. However, there are still no standard screening criteria for VRE, which poses a significant threat to patient safety. Our study aimed to develop and evaluate an artificial intelligence (AI)-based approach for identifying and predicting of at-risk patients who could assist infection prevention and control staff through a human-in-the-loop approach. We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. Our model achieves a macro F1 score of 0.880 on the task (sensitivity of 0.808, specificity of 0.942). The parameters with the strongest impact on the prediction are the codes for clinical diagnosis (ICD) and operations/procedures (OPS), which are integrated as high-dimensional patient node features in our model. We demonstrate that modeling a “living” hospital with a GNN is a promising approach for the early detection of potential VRE carriers. This proves that AI-based tools combining heterogeneous information types can predict VRE carriage with high sensitivity and could therefore serve as a promising basis for future automated infection prevention control systems. Such systems could help enhance patient safety and proactively reduce nosocomial transmission events through targeted, cost-efficient interventions. Moreover, they could enable a more effective approach to managing antimicrobial resistance.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pdig.0000821
- OA Status
- gold
- Cited By
- 2
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409325748
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409325748Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pdig.0000821Digital Object Identifier
- Title
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AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-10Full publication date if available
- Authors
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Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider‐Brachert, Udo Kruschwitz, Bärbel KieningerList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pdig.0000821Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1371/journal.pdig.0000821Direct OA link when available
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Computer science, Carriage, Graph, Artificial neural network, Artificial intelligence, Transmission (telecommunications), Machine learning, Medicine, Data mining, Pathology, Telecommunications, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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