Machine learning in heart failure: ready for prime time.

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Heart failure (HF) is a complex clinical condition requiring continuous management and monitoring.
• Traditional methods for HF management face challenges in handling large, complex patient datasets.
• Machine learning (ML) and deep learning (DL) offer novel approaches to address these challenges.

Purpose of the Study:

• To provide an updated overview of ML applications in heart failure.
• To cover key areas including diagnosis, classification, readmission prediction, and medication adherence.
• To highlight the potential of DL methods in improving HF outcomes.

Main Methods:

• Review of recent literature on ML and DL applications in heart failure.
• Analysis of studies focusing on diagnostic accuracy, predictive modeling, and treatment support.
• Synthesis of findings related to traditional ML and advanced DL techniques.

Main Results:

• ML techniques demonstrate potential in enhancing HF diagnosis and classification.
• ML models show promise in predicting patient readmissions and improving medication adherence.
• Deep learning methods are emerging as powerful tools for uncovering complex patterns in big medical data for HF.

Conclusions:

• ML and DL are increasingly valuable tools for optimizing heart failure management.
• These technologies can lead to improved patient outcomes, better cost-effectiveness, and enhanced clinical decision-making.
• Continued research and implementation of ML/DL in HF are warranted.