Risk Factor Assessment and Predictive Modeling for Ventilator‐Associated Pneumonia: Design and Clinical Implementation of an Artificial Intelligence‐Enhanced Early Detection Framework Using Multisource Data Analytics

Introduction Ventilator‐associated pneumonia (VAP) is associated with poor patient outcomes. Early identification of high‐risk patients remains a major clinical challenge. We aimed to develop and validate a multimodal hybrid neural network (MM‐HNN) for improved VAP prediction by integrating multisource data from a retrospective cohort. Methods This single‐center, retrospective study analyzed data from 213 adult patients who received invasive mechanical ventilation for >48 h. The MM‐HNN incorporated three data types: 1) computed tomography (CT) features quantifying consolidation volume through three‐dimensional residual neural network‐50; 2) dynamic ventilator parameters including fraction of inspired oxygen and positive end‐expiratory pressure analyzed via long short‐term memory networks; and 3) clinical predictors refined via least absolute shrinkage and selection operator regression to identify six key variables. Results The model achieved an area under the curve of 0.86 (95% confidence interval: 0.80–0.91), outperforming the clinical pulmonary infection score ( p = 0.021). SHapley Additive exPlanation analysis revealed Acute Physiology and Chronic Health Evaluation II score and CT consolidation volume as primary contributors. The system provided early warnings with 87.5% accuracy (median lead time: 1.5 days), which was associated with a significant increase in appropriate antibiotic use from 68.3% to 92.1% ( p = 0.016). Conclusion The MM‐HNN demonstrates the feasibility of accurate, interpretable VAP risk prediction through multimodal data integration. This artificial intelligence framework provides a clinically actionable tool for dynamic risk assessment, enabling preemptive interventions and improved antibiotic stewardship.