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XAI Interpretation of Graph Neural Network Models: Towards Trustworthy Diagnosis of Bundle Branch Blocks

Year: 2025

Type: conference-paper

Abstract: Introduction: The application of Artificial Intelligence (AI) in clinical research is progressing rapidly. In the context of electrocardiogram (ECG) analysis, conventional approaches treat signals as waveforms or tabular data. Graph Neural Networks (GNNs) are particularly well-suited for modelling data [for full text, please go to the a.m. URL]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25gmds130

Authors: Maurer, Miriam Cindy, Hempel, Philip, Steinhaus, Kristin, Krefting, Dagmar, Spicher, Nicolai, Hauschild, Anne-Christin

Institutions: Universitätsmedizin Göttingen, Campus-Institut Data Science (CIDAS), University of Göttingen, Technical University of Denmark, Justus-Liebig-Universität Gießen

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7103878566

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