Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
<|reference_start|>Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling: Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment. To address these critical issues, we propose ECG-ReGen, a retrieval-based approach for ECG-to-text report generation and question answering. Our method leverages a self-supervised learning for the ECG encoder, enabling efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries, with the potential of improving patient care. Experiments conducted on the PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance in both in-domain and cross-domain scenarios for report generation. Furthermore, our approach exhibits competitive performance on ECG-QA dataset compared to fully supervised methods when utilizing off-the-shelf LLMs for zero-shot question answering. This approach, effectively combining self-supervised encoder and LLMs, offers a scalable and efficient solution for accurate ECG interpretation, holding significant potential to enhance clinical decision-making.<|reference_end|>
@article{tang2024electrocardiogram,
    title={Electrocardiogram Report Generation and Question Answering via
  Retrieval-Augmented Self-Supervised Modeling},
    author={Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, Aaqib Saeed},
    journal={arXiv preprint arXiv:2409.08788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08788},
    primaryClass={cs.LG}
}