Risk Stratification

Risk stratification is a process used in healthcare to categorize patients based on their likelihood of experiencing adverse health outcomes. This process often employs statistical models and machine learning algorithms, such as logistic regression and random forests, to analyze patient data, including demographics, medical history, and clinical indicators. The output of risk stratification is typically a risk score that helps clinicians identify high-risk patients who may benefit from targeted interventions. The effectiveness of risk stratification can be evaluated using metrics such as the area under the receiver operating characteristic curve (AUC-ROC), which measures the model's ability to distinguish between different risk levels. By enabling personalized care plans, risk stratification plays a pivotal role in preventive medicine and resource allocation within healthcare systems.