MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
arxiv(2024)
摘要
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for
monitoring cardiac conditions and is crucial in assisting clinicians. Recent
studies have concentrated on classifying cardiac conditions using ECG data but
have overlooked ECG report generation, which is time-consuming and requires
clinical expertise. To automate ECG report generation and ensure its
versatility, we propose the Multimodal ECG Instruction Tuning (MEIT) framework,
the first attempt to tackle ECG report generation with LLMs and multimodal
instructions. To facilitate future research, we establish a benchmark to
evaluate MEIT with various LLMs backbones across two large-scale ECG datasets.
Our approach uniquely aligns the representations of the ECG signal and the
report, and we conduct extensive experiments to benchmark MEIT with nine
open-source LLMs using more than 800,000 ECG reports. MEIT's results underscore
the superior performance of instruction-tuned LLMs, showcasing their
proficiency in quality report generation, zero-shot capabilities, and
resilience to signal perturbation. These findings emphasize the efficacy of our
MEIT framework and its potential for real-world clinical application.
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