DTL: a language to assist cardiologists in improving classification algorithms.

Area of Science:

• Cardiology
• Computer Science
• Medical Informatics

Background:

• Heuristic classifiers for electrocardiogram (ECG) diagnosis are often complex and difficult to develop.
• Current methods require computer experts to translate cardiologists' reasoning, hindering verification and understanding.
• The complexity obscures how classification programs arrive at specific results.

Purpose of the Study:

• To present a novel environment simplifying the development and refinement of heuristic classifiers for ECG diagnosis.
• To enable cardiologists to directly express their diagnostic reasoning in a computer-understandable format.
• To improve transparency and verifiability in complex diagnostic classification programs.

Main Methods:

• Introduction of a new language, Decision Tree Language (DTL), designed for cardiologists.
• Development of an interpreter and translator for the DTL language.
• Detailed discussion of the design considerations, structure, and capabilities of the DTL environment.

Main Results:

• DTL allows cardiologists to articulate classification algorithms in a familiar manner.
• The interpreter and translator facilitate the implementation and verification of these algorithms.
• The environment addresses the challenges of complexity and lack of transparency in heuristic classifier development.

Conclusions:

• The presented environment, featuring DTL, significantly simplifies the creation and validation of ECG diagnostic classifiers.
• It empowers cardiologists by allowing direct input of their diagnostic logic.
• This approach enhances the usability and trustworthiness of complex diagnostic classification systems.