Summary of Electrocardiogram Report Generation and Question Answering Via Retrieval-augmented Self-supervised Modeling, by Jialu Tang et al.
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
by Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, Aaqib Saeed
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes ECG-ReGen, a novel retrieval-based approach for generating comprehensive electrocardiogram (ECG) reports and answering related questions. The method leverages self-supervised learning for the ECG encoder, allowing efficient similarity searches and report retrieval. By combining pre-training with dynamic retrieval and Large Language Model (LLM)-based refinement, ECG-ReGen analyzes ECG data and answers queries accurately. Experiments on PTB-XL and MIMIC-IV-ECG datasets demonstrate superior performance for in-domain and cross-domain report generation. Additionally, the 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 scalable and efficient solution holds significant potential to enhance clinical decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if doctors could quickly understand heart rhythms and answer questions about them with ease. A new approach called ECG-ReGen makes this possible by using artificial intelligence to analyze electrocardiogram (ECG) data and generate reports. This method is efficient, accurate, and can even answer questions without needing lots of training data. Researchers tested it on two large datasets and found that it performed well in both cases. This breakthrough has the potential to improve patient care by providing doctors with valuable insights and information. |
Keywords
» Artificial intelligence » Encoder » Large language model » Question answering » Self supervised » Supervised » Zero shot
