Towards Practical Multimodal Hospital Outbreak Detection

2026-03-20

AI Summary

This study addresses the high cost and prolonged turnaround time of whole-genome sequencing, which hinder its routine use for hospital outbreak surveillance in resource-limited settings. To overcome this limitation, the authors propose a novel, actionable tiered monitoring framework that systematically integrates three readily available clinical data streams—MALDI-TOF mass spectrometry profiles, antimicrobial resistance phenotypes, and electronic health records—into a machine learning model for rapid and cost-effective outbreak detection. The approach substantially reduces reliance on genomic sequencing while significantly enhancing detection performance across multiple pathogen species. Moreover, the multimodal fusion strategy precisely identifies high-risk transmission routes associated with invasive procedures and frequent clinical workflows, thereby offering targeted opportunities for infection control interventions.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in outbreak investigations, its substantial costs and turnaround times limit its feasibility for routine surveillance, especially in less-equipped facilities. We explore three modalities as rapid alternatives: matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry, antimicrobial resistance (AR) patterns, and electronic health records (EHR). We present a machine learning approach that learns discriminative features from these modalities to support outbreak detection. Multi-species evaluation shows that the integration of these modalities can boost outbreak detection performance. We also propose a tiered surveillance paradigm that can reduce the need for WGS through these alternative modalities. Further analysis of EHR information identifies potentially high-risk contamination routes linked to specific clinical procedures, notably those involving invasive equipment and high-frequency workflows, providing infection prevention teams with actionable targets for proactive risk mitigation

Problem

• outbreak detection
• hospital surveillance
• whole genome sequencing
• multimodal data
• infection control

Innovation

• multimodal integration
• outbreak detection
• machine learning
• MALDI-TOF
• electronic health records