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This research proposes a unified multimodal deep learning framework for early sepsis prediction, integrating physiological data, laboratory measurements, and clinical notes through self-supervised learning and causal modeling. The study addresses gaps in current literature, such as the reliance on fully labeled data and lack of uncertainty quantification, aiming to enhance accuracy and clinical trust. Expected outcomes include a reliable prediction system validated on large-scale ICU datasets, improving ear
- Author
- Zeeshan
- Language
- EN