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

Abstract:Early sepsis warning from ICU records can be cast as a structure-preserving prediction problem. A model needs to detect deterioration from irregular measurements while keeping each alert connected to the physiological signals that support it. Many temporal models fuse clinical variables into a patient-level representation, supporting scalar risk prediction but weakening the structure needed for clinical decomposition. We present SepsisLens, which preserves variable-indexed temporal states until risk composition. Observation-aware representations encode each variable's dynamics and measurement history, while a shared temporal encoder models each trajectory without collapsing the variable axis. The StructuredRiskHead composes multi-horizon risk from explicit variable-level and organ-level components. We evaluate SepsisLens on three public ICU cohorts and one private-hospital cohort under a common pre-onset protocol. SepsisLens achieves strong discrimination on all four cohorts and lower alert burden at matched event recall on MIMIC-IV. Structural ablations support the design, while input-side masking shows that the ranked components reflect variables with greater influence on prediction.

Machine Learning (cs.LG)

arXiv:2610.08046 [cs.LG]

(or arXiv:2610.08046v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.08046 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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