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Updated: May 23, 2025
Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital
Jiaming Cui1,2, Jack Heavey3, Eili Klein4,5,6
1College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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.
Insights
A new NeurABM framework accurately identifies healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) by combining neural networks and agent-based models, improving patient care and forecasting nosocomial infections.
Area of Science:
- Computational epidemiology
- Infectious disease modeling
- Machine learning in healthcare
Background:
- Healthcare-associated infections (HAIs) caused by multi-drug resistant organisms (MDROs) present a major healthcare challenge.
- Distinguishing between imported and nosocomial infections is difficult due to asymptomatic cases and testing limitations.
Purpose of the Study:
- To develop a novel framework, NeurABM, integrating neural networks and agent-based models (ABM).
- To improve the identification of patient importation of infections and forecasting of nosocomial infections.
Main Methods:
- Developed NeurABM, a hybrid model combining neural networks for patient-level predictions and ABM for transmission dynamics.
- Utilized electronic health record (EHR) data within the integrated framework.
- Simultaneously learned importation predictions and infection identification models.
Main Results:
- NeurABM significantly outperformed existing methods in identifying importation cases.
- The framework demonstrated superior accuracy in forecasting future nosocomial infections.
- Successfully leveraged EHR data and mechanistic insights for improved prediction.
Conclusions:
- NeurABM represents a breakthrough in accurately identifying infection importation and predicting nosocomial infections.
- The integrated approach overcomes limitations of purely transmission or machine learning models.
- Offers a powerful tool for clinical practice to manage MDRO spread.
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