2025 challenge KP BSI
收藏DataCite Commons2025-06-16 更新2026-05-07 收录
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Research Proposal: AI-Driven Precision Surveillance of Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infections – A Critical WHO Priority Pathogen
Carbapenem-resistant Klebsiella pneumoniae bloodstream infections (CR-Kp BSI) represent a paramount global health threat, recently designated by WHO (2024) as a critical priority pathogen due to their alarming mortality rates and rapid global dissemination. Notably, CR-Kp accounts for more than 50% of all carbapenem-resistant Enterobacterales infections, making it the most prevalent and lethal antimicrobial resistance threat in hospital settings worldwide.
Our study addresses this crisis through an innovative AI-powered framework: (1) employing spatiotemporal deep learning to decode CR-Kp BSI transmission dynamics pre/post-COVID-19 across 80+ countries, identifying critical inflection points in resistance trends; (2) developing a real-time risk prediction tool using ensemble machine learning (XGBoost/Transformer models) to flag high-risk patients at admission by integrating 50+ clinical-demographic variables; (3) conducting precision health economic analyses to quantify the catastrophic financial burden (ICU costs, extended hospitalization, and salvage therapy expenses); (4) establishing mortality-associated biomarkers through explainable AI (SHAP analysis) of treatment regimens and host factors; and (5) deploying plasmid-centric genomics to trace resistant gene transmission networks using graph neural networks.
By synergizing WHO’s AMR surveillance guidelines with cutting-edge AI/ML technologies, this research will deliver:
•Actionable early-warning systems for hospital outbreaks
• Cost-effective interventions targeting high-burden settings
• Genomically-informed containment strategies against high-risk plasmids
• A template for AI-enhanced AMR surveillance adaptable to other priority pathogens
Leveraging multinational datasets (ATLAS) representing >500,000 isolates, our approach bridges the critical gap between AMR big data and clinically implementable solutions, directly supporting the WHO Global Action Plan on AMR.
提供机构:
Vivli
创建时间:
2025-06-16



