[Research Advances in Big Data-based Diagnosis and Therapy of Heart Failure]

Area of Science:

• Cardiology
• Medical Informatics
• Data Science

Background:

• Heart failure presents a substantial global health challenge with high prevalence and mortality rates.
• Effective management strategies are crucial for improving patient outcomes and reducing healthcare burdens.
• Current management approaches can benefit from advanced analytical techniques.

Purpose of the Study:

• To review the existing literature on the application of big data analysis in heart failure.
• To validate the utility of big data analytics in enhancing the diagnosis of heart failure.
• To assess the role of big data in the classification and prognostic prediction for heart failure patients.

Main Methods:

• Systematic literature review of studies employing big data analysis in heart failure.
• Analysis of diagnostic accuracy improvements attributed to big data methods.
• Evaluation of big data's contribution to patient stratification and risk prediction models.

Main Results:

• Big data analysis demonstrates potential to refine heart failure diagnosis and classification.
• Prognostic prediction models utilizing big data show promise in identifying high-risk patients.
• Integration of big data analytics can lead to more personalized and effective heart failure management.

Conclusions:

• Big data analysis is a valuable tool for advancing heart failure care.
• Further research and implementation are warranted to fully leverage big data in clinical practice.
• The findings support the integration of big data strategies for improved heart failure patient outcomes.