Work

CAMEL: An ECG Language Model for Forecasting Cardiac Events

Year: 2026

Type: preprint

Abstract: , a new benchmark that we introduce for forecasting arrhythmias. CAMEL is on par with or surpasses ELMs and fully supervised baselines both in- and out-of-distribution, achieving SOTA results on ECGBench (+7.0% absolute average gain) as well as ECGForecastBench (+12.4% over fully supervised models and +21.1% over zero-shot ELMs).

Source: PubMed

DOI: 10.48550/arxiv.2602.15677

Authors: Velingker, Neelay, Solko-Breslin, Alaia, Keoliya, Mayank, Choi, Seewon, Xin, Jiayi, Marathe, Anika, Oraii, Alireza, Deo, Rajat, Khatana, Sameed, Alur, Rajeev, Naik, Mayur, Wong, Eric

Institution: University of Pennsylvania

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: University of Pennsylvania, National Institutes of Health, Advanced Research Projects Agency

Award: Advancing Neurosymbolic AI for Smart and Trustworthy Healthcare

OpenAlex ID: W7130326804

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