Advancing Congestive Heart Failure Management through Digital Twin Integration

Congestive heart failure (CHF) imposes a substantial burden on global health systems. This is affecting millions and necessitating significant healthcare resources. This study introduces a CHF-specific digital twin framework for personalization of CHF management. We detail the creation of digital twins through the integration of diverse patient data, including real-time physiological parameters, genomic data and longitudinal health records. Our approach utilizes a digital twin that offers a dynamic representation of the patient’s health. This approach addresses the challenges in CHF management, such as risk stratification, disease progression and treatment personalization. This study's main contribution is integrating mechanistic and statistical models using artificial intelligence to improve predictive analytics' accuracy for real-time clinical decisions. We extend our investigation to encompass ethical considerations by giving importance of patient consent, data privacy and ethical use of predictive analytics. This study advocates for the necessity of transparent communication and education to cultivate trust in this innovative technology among healthcare providers and patients.