搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well-annotated ECG corpora are scarce due to cost, privacy, and workflow constraints. Generating ECGs can aid mechanistic understanding of cardiac electrical activity, enable the construction of large, heterogeneous, and unbiased datasets, and facilitate privacy-preserving data sharing. Generating realistic ECG signals from clinical context is important yet underexplored. Recent work has leveraged diffusion models for text-to-ECG generation, but two challenges remain: (i) existing methods often overlook physiological simulator knowledge of cardiac activity; and (ii) they ignore broader, experience-based clinical knowledge grounded in real-world practice.

PTB-XL is a large public 12-lead electrocardiography dataset with diagnostic statements and waveform records. It is a standard benchmark for ECG classification, cardiac abnormality detection, clinical signal representation learning, and robust evaluation of biosignal models.

MIMIC-IV-ECG is a large deidentified electrocardiogram dataset linked to the MIMIC-IV clinical data ecosystem. It supports ECG classification, arrhythmia detection, representation learning, and multimodal modeling with structured EHR context.