Abstract:The electrocardiogram (ECG) is the gold standard for non-invasive diagnosis of cardiac pathologies and is a fundamental pillar of cardiovascular medicine. Recent progress in deep learning has led to the development of robust automated classifiers that achieve high performance by processing raw physiological signals. However, in clinical practice, diagnosis is rarely based solely on the signal. Cardiologists commonly support their interpretation with the patient's characteristics and the specific data-acquisition context. Despite this, most current algorithms remain restricted to signal-only analysis, failing to integrate technical metadata and demographic variables. This paper proposes Contextual Language-Informed Cardiac pathology classification (CLIC), a multimodal framework that significantly enhances diagnostic precision by encoding these variables through natural language. We demonstrate that translating patient-level contextual data into descriptive text provides an informative anchor that helps the model disambiguate complex physiological patterns. We further investigate the use of Large Language Models to synthesize richer clinical descriptions and observe that, while these generated texts remain competitive, controlled template-based contextual clinical text leads to consistent improvements in downstream classification performance.
| Comments: | 6 pages, 2 figures, accepted at the ICLR 2026 Workshop on Time Series in the Age of Large Models (TSALM) |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.2.6; J.3 |
| Cite as: | arXiv:2605.19132 [cs.LG] |
| (or arXiv:2605.19132v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19132 arXiv-issued DOI via DataCite (pending registration) |
From: Giovani Lucafó [view email]
[v1]
Mon, 18 May 2026 21:31:51 UTC (594 KB)
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