Abstract
Recently, Large Language Models (LLMs) have become essential players in the deep learning domain. While their capabilities are evident across various textual tasks, this study aims to bridge the gap and explore the potential of leveraging LLMs in diagnosing cardiac diseases and sleep apnea from Electrocardiography (ECG). Earlier work touched on converting ECG signals into text for LLMs, but a comprehensive LLM-based approach for dealing with more complicated symptoms remains relatively unexplored. To investigate the ECG diagnosis with an LLM-based approach, our research introduces a zero-shot retrieval-augmented diagnosis technique. We have built databases filled with specific domain knowledge for cardiac symptom and sleep apnea diagnosis, which encourages the LLMs from merely relying on the inherent LLM knowledge to a more holistic pipeline from carefully crafting prompts and infusing expert knowledge to guide LLMs. We evaluate the proposed approach on two datasets for diagnosing arrhythmia and sleep apnea, respectively. The evaluation results indicate that our zero-shot approach not only surpasses previous few-shot LLM-based methods but is also competitive with supervised learning techniques fully trained on extensive datasets.
Original language | English (US) |
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Pages (from-to) | 650-663 |
Number of pages | 14 |
Journal | Proceedings of Machine Learning Research |
Volume | 225 |
State | Published - 2023 |
Event | 3rd Machine Learning for Health Symposium, ML4H 2023 - New Orleans, United States Duration: Dec 10 2023 → … |
Keywords
- Apnea
- Arrhythmia
- Electrocardiogram (ECG)
- Large Language Model (LLM)
- Retrieval-Augmented Generation (RAG)
- Zero-Shot Learning
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability