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arXiv Machine Learning By Yikun Ou, Wei Li

SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning

Read the original on arXiv Machine Learning →

SepsisLens is a new model for early sepsis warning that preserves the structure of individual physiological signals while predicting risk. It uses observation-aware representations to encode each variable’s dynamics and a shared temporal encoder to maintain variable axes, then composes multi-horizon risk through a StructuredRiskHead that includes variable-level and organ-level components. The model was evaluated on three public ICU cohorts and one private-hospital cohort, achieving strong discrimination and lower alert burden at matched recall on MIMIC-IV, with ablations confirming the design choices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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