Decision Support Systems in HF based on Deep Learning Technologies.

Area of Science:

• Artificial Intelligence in Medicine
• Cardiology Informatics
• Computational Health

Background:

• Deep learning (DL) applications are expanding within healthcare.
• Heart failure (HF) management is a key area for AI integration.
• Existing methods for HF analysis can be enhanced by advanced computational techniques.

Purpose of the Study:

• To review the current state of DL techniques applied to HF management.
• To assess DL's maturity in HF diagnosis, prognosis, and re-hospitalization risk prediction.
• To explore DL's application across structured electronic health records, physiological signals, and imaging modalities.

Main Methods:

• Systematic review of DL applications in HF.
• Analysis of DL performance on structured EHR data, physiological signals, and medical imaging.
• Comparison of DL outcomes against conventional and machine learning approaches.

Main Results:

• DL integration of diverse data sources yields more accurate HF patient outcomes than conventional methods.
• DL demonstrates very high performance in HF diagnosis using image and signal processing.
• DL application to EHR data for prediction shows promising but improvable results, needing enhanced multisource data integration.

Conclusions:

• DL offers potential for more efficient HF care and improved patient outcomes.
• Further research is necessary to address limitations in generalizability, transparency, and explicability of DL evidence.
• DL advancements can significantly impact the future of heart failure management and prediction.