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Long COVID (post-acute sequelae of SARS-CoV-2 infection, PASC) affects roughly 10–30% of COVID-19 survivors and is marked by persistent symptoms such as fatigue, cognitive dysfunction (“brain fog”), shortness of breath, loss of smell, and post-exertional malaise that can last for months or years, while its underlying biological mechanisms and validated diagnostic biomarkers remain unclear. The condition is highly heterogeneous, with patients showing different recovery patterns and no clearly defined clinical subtypes, and the scarcity of labeled datasets further limits the use of supervised machine learning methods for phenotyping. To address this, we propose a self-supervised contrastive multi-view learning framework that integrates three temporal data modalities—pre-infection electronic health records, acute-phase clinical and biomarker data (e.g., CRP, ferritin, D-dimer, lymphocyte counts), and post-acute symptom trajectories—using separate encoders and a shared latent space aligned through contrastive learning without requiring phenotype labels, followed by unsupervised clustering to identify potential subtypes. By exploiting the natural temporal linkage within each patient and contrasts across patients, this approach enables data-driven discovery of long COVID phenotypes, supports early prediction of subgroup membership, and may ultimately inform personalized treatment strategies, clinical trial design, and improved understanding of disease mechanisms.

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.