AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers.
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 significa...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000821 |
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| _version_ | 1849725768970534912 |
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| author | Gregor Donabauer Anca Rath Aila Caplunik-Pratsch Anja Eichner Jürgen Fritsch Martin Kieninger Susanne Gaube Wulf Schneider-Brachert Udo Kruschwitz Bärbel Kieninger |
| author_facet | Gregor Donabauer Anca Rath Aila Caplunik-Pratsch Anja Eichner Jürgen Fritsch Martin Kieninger Susanne Gaube Wulf Schneider-Brachert Udo Kruschwitz Bärbel Kieninger |
| author_sort | Gregor Donabauer |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4bfc3cb6bef5486ca89f876f2aa9c575 |
| institution | DOAJ |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-4bfc3cb6bef5486ca89f876f2aa9c5752025-08-20T03:10:23ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-04-0144e000082110.1371/journal.pdig.0000821AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers.Gregor DonabauerAnca RathAila Caplunik-PratschAnja EichnerJürgen FritschMartin KieningerSusanne GaubeWulf Schneider-BrachertUdo KruschwitzBärbel KieningerThe 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.https://doi.org/10.1371/journal.pdig.0000821 |
| spellingShingle | Gregor Donabauer Anca Rath Aila Caplunik-Pratsch Anja Eichner Jürgen Fritsch Martin Kieninger Susanne Gaube Wulf Schneider-Brachert Udo Kruschwitz Bärbel Kieninger AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. PLOS Digital Health |
| title | AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. |
| title_full | AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. |
| title_fullStr | AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. |
| title_full_unstemmed | AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. |
| title_short | AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. |
| title_sort | ai modeling for outbreak prediction a graph neural network approach for identifying vancomycin resistant enterococcus carriers |
| url | https://doi.org/10.1371/journal.pdig.0000821 |
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