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Sepsis prediction models perform poorly when transferred between ICUs due to demographic and practice variation, leading to substantial performance drops caused by differences in patient populations, admission criteria, and data recording standards, which limits reliable deployment across healthcare systems. Retraining models from scratch requires large labeled datasets that many ICUs lack due to cost, time, and resource limitations, making it difficult for low-resource settings to develop or adopt effective predictive tools. We propose a meta-learning approach that enables rapid adaptation of sepsis prediction models using few-shot local data, leveraging pre-training across multiple ICUs to support fast personalization in new environments. The framework includes meta-training across diverse source ICUs to learn a generalizable initialization and meta-adaptation at the target ICU using only a few gradient updates on limited data, enabling efficient few-shot learning. This approach improves sepsis prediction in low-resource and heterogeneous ICU settings by reducing data requirements and increasing robustness to demographic shifts, supporting more equitable access to AI tools in critical care. The proposed framework enables efficient and fair deployment of sepsis prediction models across diverse ICUs, bridging resource gaps and improving scalability and adaptability of clinical AI systems globally.

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.