Identification of key features associated with 3-year mortality in hypertensive crisis patients using machine learning

Key Points

• Mortality in hypertensive crisis patients is significantly associated with key risk factors identified through machine learning.
• A predictive modeling approach reveals critical features that influence 3-year mortality risk in this population.
• The analysis utilized advanced machine learning techniques to assess the relationships between various health indicators and outcomes.
• Improving prediction accuracy may enhance patient management and intervention strategies in clinical settings.