Risk Stratification

Risk stratification aims to categorize individuals into groups with varying probabilities of experiencing a specific outcome, such as disease progression or adverse events, enabling personalized interventions. Current research heavily utilizes machine learning, particularly deep learning architectures and gradient boosting machines, to improve the accuracy of risk prediction across diverse health conditions, often incorporating multimodal data (e.g., imaging, clinical records, biomarkers). These advancements offer the potential for more precise and effective clinical decision-making, leading to improved patient care and resource allocation by optimizing treatment strategies and identifying high-risk individuals for timely intervention.