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Wearable electrocardiogram (ECG) devices such as smartwatches and ambulatory monitors generate large-scale continuous cardiac data suitable for arrhythmia detection in real-world settings. However, the development of supervised machine learning models is limited by the scarcity of expert-annotated ECG data, class imbalance due to rare arrhythmias, and privacy constraints that restrict data sharing. These challenges make it difficult for traditional deep learning approaches to scale effectively in clinical applications.This work proposes a self-supervised contrastive learning framework that leverages large volumes of unlabeled wearable ECG data to learn meaningful cardiac representations. Using ECG-specific data augmentations, the model is trained to maximize agreement between different views of the same signal while distinguishing between different segments. A deep encoder produces latent embeddings, which are optimized through a contrastive loss, and later adapted for arrhythmia classification using a lightweight classifier with minimal labeled data.The proposed approach reduces dependence on expert annotations, improves generalization across devices and populations, and supports privacy-preserving training. Overall, it offers a scalable and efficient pathway for wearable-based arrhythmia detection, potentially enabling earlier diagnosis and broader deployment of cardiac AI systems in resource-limited healthcare settings.

Emergency department chief complaints and triage notes are early indicators of health changes during infectious disease outbreaks. These records, made before confirmatory testing, provide a presyndromic view of population health. Traditional syndromic surveillance relies on predefined syndrome categories, which may not align with novel pathogens. Early outbreaks often present as sparse, ambiguous symptom clusters, resulting in few labeled examples for automated detection. This framework suggests using contrastive learning with prototypical networks for few-shot detection of emerging infectious disease syndromes from free-text notes. It leverages historical data to create a robust clinical text embedding space, with a small set of labeled examples defining new syndromes. The system includes a contrastive pre-training encoder, prototypical network, and few-shot classifier. The encoder learns from unlabelled historical notes, and the prototypical network creates syndrome prototypes from a few labeled examples. This framework is designed for situations where public health officials observe early suspect cases but lack mature labeled datasets. It can identify early clusters by comparing incoming notes to emerging syndrome prototypes. Contrastive learning with prototypical networks enables proactive presyndromic surveillance, allowing rapid adaptation during the early phase of an outbreak without relying on large labeled datasets.