Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings
Abstract Healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) pose a significant challenge for healthcare systems. Patients can arrive at hospitals already infected ("importation”) or acquire infections during their stay ("nosocomial infection”). Many cases,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01529-x |
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| author | Jiaming Cui Jack Heavey Eili Klein Gregory R. Madden Costi D. Sifri Anil Vullikanti B. Aditya Prakash |
| author_facet | Jiaming Cui Jack Heavey Eili Klein Gregory R. Madden Costi D. Sifri Anil Vullikanti B. Aditya Prakash |
| author_sort | Jiaming Cui |
| collection | DOAJ |
| description | Abstract Healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) pose a significant challenge for healthcare systems. Patients can arrive at hospitals already infected ("importation”) or acquire infections during their stay ("nosocomial infection”). Many cases, often asymptomatic, complicate rapid identification due to testing limitations and delays. Although recent advancements in mathematical modeling and machine learning have aimed to identify at-risk patients, these methods face challenges: transmission models often overlook valuable electronic health record (EHR) data, while machine learning approaches typically lack mechanistic insights into underlying processes. To address these issues, we propose NeurABM, a novel framework that integrates neural networks and agent-based models (ABM) to leverage the strengths of both methods. NeurABM simultaneously learns a neural network for patient-level importation predictions and an ABM for infection identification. Our findings show that NeurABM significantly outperforms existing methods, marking a breakthrough in accurately identifying importation cases and forecasting future nosocomial infections in clinical practice. |
| format | Article |
| id | doaj-art-d2b18dbbcf7b4c0ba96cdd15221abd21 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-d2b18dbbcf7b4c0ba96cdd15221abd212025-08-20T01:57:40ZengNature Portfolionpj Digital Medicine2398-63522025-03-018111110.1038/s41746-025-01529-xIdentifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settingsJiaming Cui0Jack Heavey1Eili Klein2Gregory R. Madden3Costi D. Sifri4Anil Vullikanti5B. Aditya Prakash6College of Computing, Georgia Institute of TechnologyDepartment of Computer Science, University of VirginiaCenter for Disease Dynamics, Economics & PolicyDivision of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of MedicineDivision of Infectious Diseases & International Health, Department of Medicine, University of Virginia School of MedicineDepartment of Computer Science, University of VirginiaCollege of Computing, Georgia Institute of TechnologyAbstract Healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) pose a significant challenge for healthcare systems. Patients can arrive at hospitals already infected ("importation”) or acquire infections during their stay ("nosocomial infection”). Many cases, often asymptomatic, complicate rapid identification due to testing limitations and delays. Although recent advancements in mathematical modeling and machine learning have aimed to identify at-risk patients, these methods face challenges: transmission models often overlook valuable electronic health record (EHR) data, while machine learning approaches typically lack mechanistic insights into underlying processes. To address these issues, we propose NeurABM, a novel framework that integrates neural networks and agent-based models (ABM) to leverage the strengths of both methods. NeurABM simultaneously learns a neural network for patient-level importation predictions and an ABM for infection identification. Our findings show that NeurABM significantly outperforms existing methods, marking a breakthrough in accurately identifying importation cases and forecasting future nosocomial infections in clinical practice.https://doi.org/10.1038/s41746-025-01529-x |
| spellingShingle | Jiaming Cui Jack Heavey Eili Klein Gregory R. Madden Costi D. Sifri Anil Vullikanti B. Aditya Prakash Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings npj Digital Medicine |
| title | Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings |
| title_full | Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings |
| title_fullStr | Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings |
| title_full_unstemmed | Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings |
| title_short | Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings |
| title_sort | identifying and forecasting importation and asymptomatic spreaders of multi drug resistant organisms in hospital settings |
| url | https://doi.org/10.1038/s41746-025-01529-x |
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