Masters in Public Health, USA.
Research Article
Magna Scientia Advanced Biology and Pharmacy, 2023, 10(02), 131-147
Article DOI: 10.30574/msabp.2023.10.2.0081
Received on 13 November 2023; revised on 27 December 2023; accepted on 30 December 2023
Healthcare-associated infections (HAIs) remain a persistent source of morbidity, mortality, and operational strain across global healthcare systems, underscoring the need for earlier, more accurate detection mechanisms. Traditional surveillance approaches often reliant on manual chart reviews, isolated clinical indicators, or retrospective laboratory results frequently fail to identify infection risks in real time. Recent advances in multimodal deep learning have created a pathway toward more proactive and context-aware HAI detection by integrating diverse, high-velocity data streams. From a broader perspective, multimodal architectures combine heterogeneous clinical inputs to form richer representations of patient status than single-modality models can achieve. Within this expanded analytical ecosystem, clinical imaging provides spatial and morphological patterns indicative of infection onset, enabling deep convolutional networks to detect infiltrates, device-associated abnormalities, or tissue inflammation earlier than human observers. Meanwhile, vital-sign trajectories such as temperature fluctuations, respiration irregularities, and hemodynamic variability offer temporal signals that recurrent or transformer-based models can interpret as precursors to infectious deterioration. These physiological streams capture dynamic physiological shifts that imaging alone may overlook. A third yet critical dimension involves workflow disruptions: delays in medication administration, unscheduled laboratory orders, extended device dwell times, or anomalous care-team interactions. These operational signals, often ignored in traditional surveillance, serve as proxies for latent safety hazards that correlate strongly with elevated HAI risk. Graph-based and sequence-fusion models can integrate these workflow anomalies with clinical modalities, generating holistic patient-risk scores that support earlier intervention. Narrowing the focus, this study emphasizes how multimodal deep learning pipelines that merge imaging features, vital-sign dynamics, and workflow irregularities can enhance predictive accuracy, reduce false alarms, and enable real-time infection prevention strategies. Such integrated frameworks represent a transformative advancement toward precision infection control and more resilient healthcare delivery systems.
Multimodal learning; Deep learning; HAI detection; Clinical imaging; Vital-sign analytics; Workflow disruptions
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Deborah Uzor. Multimodal deep learning models combining clinical imaging, vital-sign patterns, and workflow disruptions for early HAI detection. Magna Scientia Advanced Biology and Pharmacy, 2023, 10(2), 131-147. Article DOI: https://doi.org/10.30574/msabp.2023.10.2.0081