12^th International Heart Conference

Heart Failure Prediction

Heart failure is a major public health concern, affecting millions of individuals worldwide and contributing to high rates of hospitalization and mortality. Early detection of worsening heart failure is critical for preventing complications and improving patient quality of life. Digital health platforms equipped with artificial intelligence play a pivotal role in predicting heart failure exacerbations by continuously analyzing patient data, including heart rate, blood pressure, weight, oxygen saturation, and activity levels. Machine learning models can identify patterns that indicate early signs of fluid overload or cardiac dysfunction before clinical symptoms become severe. By generating real-time alerts for patients and healthcare providers, AI systems enable timely adjustments to medications, dietary recommendations, or lifestyle interventions. Remote monitoring also allows patients to remain in their homes while clinicians track their condition, reducing hospital readmissions and healthcare costs. Furthermore, AI-driven predictive analytics can personalize treatment plans by identifying patient-specific risk factors and suggesting tailored interventions. By combining continuous monitoring, predictive modeling, and personalized care, artificial intelligence transforms heart failure management, enhancing both patient outcomes and healthcare efficiency.