Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling

• URL: http://arxiv.org/abs/2409.08788v1
• Date: Fri, 13 Sep 2024 12:50:36 GMT
• Title: Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
• Authors: Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, Aaqib Saeed,
• Abstract summary: ECG-ReGen is a retrieval-based approach for ECG-to-text report generation and question answering. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen effectively analyzes ECG data and answers related queries.
• Score: 19.513904491604794
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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