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A multimodal machine learning framework for predicting post-meal glucose spikes at 1, 3, and 6-hour intervals using CGM data, nutrition logs, clinical biomarkers, and food image embeddings.
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The project addresses a complex health-tech problem by fusing time-series data with image embeddings. However, with zero stars and no community activity, it currently exists as a personal research repository or academic reference implementation without a defensive moat or established ecosystem.
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