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[2602.15677] CAMEL: An ECG Language Model for Forecasting Cardiac Events
Summary
CAMEL is a novel ECG language model designed to forecast cardiac events by leveraging advanced machine learning techniques, achieving state-of-the-art results in ECG analysis.
Why It Matters
This research addresses a critical gap in ECG analysis by enabling the prediction of future cardiac events, which can significantly enhance patient care and intervention strategies. The introduction of CAMEL and the ECGForecastBench benchmark could lead to improved diagnostic tools in cardiology.
Key Takeaways
- CAMEL is the first ECG language model capable of forecasting cardiac events.
- It utilizes a specialized ECG encoder for better signal and text understanding.
- The model shows strong zero-shot performance across multiple tasks and datasets.
- CAMEL outperforms existing ECG language models and fully supervised baselines.
- The introduction of ECGForecastBench sets a new standard for benchmarking ECG forecasting.
Computer Science > Machine Learning arXiv:2602.15677 (cs) [Submitted on 17 Feb 2026] Title:CAMEL: An ECG Language Model for Forecasting Cardiac Events Authors:Neelay Velingker, Alaia Solko-Breslin, Mayank Keoliya, Seewon Choi, Jiayi Xin, Anika Marathe, Alireza Oraii, Rajat Deo, Sameed Khatana, Rajeev Alur, Mayur Naik, Eric Wong View a PDF of the paper titled CAMEL: An ECG Language Model for Forecasting Cardiac Events, by Neelay Velingker and 11 other authors View PDF Abstract:Electrocardiograms (ECG) are electrical recordings of the heart that are critical for diagnosing cardiovascular conditions. ECG language models (ELMs) have recently emerged as a promising framework for ECG classification accompanied by report generation. However, current models cannot forecast future cardiac events despite the immense clinical value for planning earlier intervention. To address this gap, we propose CAMEL, the first ELM that is capable of inference over longer signal durations which enables its forecasting capability. Our key insight is a specialized ECG encoder which enables cross-understanding of ECG signals with text. We train CAMEL using established LLM training procedures, combining LoRA adaptation with a curriculum learning pipeline. Our curriculum includes ECG classification, metrics calculations, and multi-turn conversations to elicit reasoning. CAMEL demonstrates strong zero-shot performance across 6 tasks and 9 datasets, including ECGForecastBench, a new benchmark that we int...