AI-BASED RISK STRATIFICATION MODELS IN EMERGENCY MEDICINE
Keywords:
artificial intelligence, risk stratification, emergency medicine, machine learning, patient prioritization, clinical decision support, explainable AI.Abstract
Emergency departments face high patient volumes and diverse clinical presentations, making rapid risk assessment essential. Traditional triage methods often rely on limited indicators and may miss complex risk patterns. This study examines AI-based risk stratification models for emergency medicine, using demographic, clinical, laboratory, and vital sign data. Machine learning algorithms, including random forests, gradient boosting, and neural networks, were trained and validated to predict patient risk. Performance was evaluated with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results show that AI models outperform conventional scoring tools in identifying high-risk patients requiring urgent care. The integration of explainable AI enhances transparency and clinical interpretability. These findings indicate that AI-driven risk stratification can improve patient prioritization, optimize resource use, and support timely decision-making in emergency care.
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