Electrocardiogram Instruction Tuning for Report Generation
arxiv(2024)
摘要
Electrocardiogram (ECG) serves as the primary non-invasive diagnostic tool
for cardiac conditions monitoring, are crucial in assisting clinicians. Recent
studies have concentrated on classifying cardiac conditions using ECG data but
have overlooked ECG report generation, which is not only time-consuming but
also 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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