Leveraging Predictive Machine Learning Models for Early Risk Identification and Clinical Outcome Mitigation in High-Risk Emergency Department Care Operations

• Abbas Ahsun

  Texas University

  Author

Emergency departments (EDs) worldwide face escalating operational pressures characterized by overcrowding, prolonged wait times, and increasing patient acuity, necessitating innovative approaches to early risk identification. While traditional early warning scores and triage systems provide standardized assessment frameworks, they demonstrate limited predictive accuracy and fail to leverage the full potential of electronic health record data. This study develops and validates a predictive machine learning framework for early risk identification and clinical outcome mitigation in high-risk ED care operations. Using a retrospective analysis of 17,481 consecutive adult ED visits over a six-month period, we implemented an XGBoost-based multimodal model integrating structured triage data (demographics, vital signs, triage acuity scores) with transformer-based embeddings derived from free-text nursing triage notes. The model achieved an ROC-AUC of 0.90 (95% CI: 0.88–0.92) and a recall of 0.77 for predicting early clinical deterioration (ICU admission or death within 7 days), substantially outperforming the National Early Warning Score (AUC 0.65). SHAP analysis identified age, respiratory rate, and systolic blood pressure as dominant predictors, with free-text embeddings providing an 8% incremental accuracy gain. The framework enables risk-based patient prioritization with a practical lead time of 15–30 minutes from triage completion. These findings demonstrate that machine learning models, when carefully designed with class weighting to prioritize high-risk detection and integrated into clinical workflows as decision support rather than replacement, offer a viable pathway to enhance ED situational awareness and improve patient outcomes. This research contributes a replicable, explainable predictive framework with direct implications for ED operations management and clinical safety protocols.

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Copyright (c) 2026 Abbas Ahsun (Author)

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