AI-Based Predictive Safety Monitoring

Abstract

AI-based predictive safety monitoring has emerged as one of the highest-impact clinical applications of machine learning in hospital settings, promising earlier detection of patient deterioration, reduced preventable adverse events, and more efficient allocation of clinical attention across monitored patient populations. The clinical evidence base has grown substantially, but comparative assessment of AI safety monitoring approaches across different architectures and clinical monitoring contexts remains sparse -- systems are typically evaluated in a single context with a single architecture, making cross-system comparison difficult. This study presents the AI Predictive Safety Monitoring Assessment Framework (APSMAF), evaluating five AI monitoring approaches -- LSTM-based vital signs deterioration prediction (LSTM-VS), gradient-boosted multi-parameter early warning (GB-MEW), transformer-based multi-modal safety monitoring (TF-MSM), federated learning cross-site safety model (FL-CSM), and explainable AI safety monitoring with clinical decision support (XAI-CDS) -- across four clinical safety monitoring contexts: post-operative surgical monitoring, ICU and critical care deterioration prevention, oncology treatment toxicity monitoring, and paediatric ward safety monitoring. Performance was scored using the Predictive Safety Effectiveness Score (PSES), a weighted composite of prediction accuracy (0.30), alert lead time (0.20), clinical interpretability (0.20), implementation feasibility (0.15), and false positive management (0.15). Explainable AI safety monitoring achieved the highest PSES (0.895) through the study's best clinical interpretability (0.960), driven by SHAP-based explanation interfaces that increased clinician alert acknowledgement rates by 34.2% and reduced alert dismissal by 28.7% compared to black-box alert systems. Federated learning ranked second (0.885) with the best false positive management (0.900). Transformer-based multi-modal monitoring (0.882) achieved the highest prediction accuracy (0.940). LSTM-based vital signs prediction (0.873) showed the best alert lead time in the ICU context.