Neural Signals Generate Clinical Notes in the Wild
This addresses the problem of automating clinical note generation for EEG analysis, benefiting healthcare professionals by reducing manual effort, though it appears incremental as it builds on existing foundation models.
The paper tackles the labor-intensive task of generating clinical reports from long-term EEG recordings by developing CELM, a clinical EEG-to-Language foundation model, which achieves 70%–95% average relative improvements in generation metrics like ROUGE-1 and METEOR, with scores increasing from 0.2–0.3 to 0.4–0.6.
Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We curate a large-scale clinical EEG dataset with $9{,}922$ reports paired with approximately $11{,}000$ hours of EEG recordings from $9{,}048$ patients. We therefore develop CELM, the first clinical EEG-to-Language foundation model capable of summarizing long-duration, variable-length EEG recordings and performing end-to-end clinical report generation at multiple scales, including recording description, background activity, epileptiform abnormalities, events/seizures, and impressions. Experimental results show that, with patient history supervision, our method achieves $70\%$--$95\%$ average relative improvements in standard generation metrics (e.g., ROUGE-1 and METEOR) from $0.2$--$0.3$ to $0.4$--$0.6$. In the zero-shot setting without patient history, CELM attains generation scores in the range of $0.43$--$0.52$, compared to baselines of $0.17$--$0.26$. CELM integrates pretrained EEG foundation models with language models to enable scalable multimodal learning. We release our model and benchmark construction pipeline at [URL].