Case-Based Reasoning for Support of the Diagnostics of Cardiovascular Diseases

Area of Science:

• Cardiology
• Artificial Intelligence
• Medical Informatics

Background:

• Cardiovascular disease diagnosis relies on complex patient data and expert interpretation.
• Coronary angiography is a critical but invasive diagnostic procedure.
• Existing decision support tools may lack comprehensive case analysis capabilities.

Purpose of the Study:

• To develop and implement a case-based reasoning (CBR) decision support system (DSS) for cardiologists.
• To assist clinicians in evaluating the need for coronary angiography.
• To enhance diagnostic accuracy by leveraging historical patient data.

Main Methods:

• The system employs case-based reasoning principles for analyzing patient data.
• It identifies similar historical cases to a new patient's profile.
• It suggests probable outcomes for potential coronary angiography examinations.

Main Results:

• The decision support system successfully retrieves similar historical cases.
• It provides predictive insights into coronary angiography results.
• Visualizations are generated to aid cardiologist decision-making.

Conclusions:

• The developed CBR-based decision support system shows promise in assisting cardiologists.
• The system aids in the critical decision-making process for coronary angiography.
• Close collaboration with cardiologists ensures clinical relevance and usability.