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.
SepsisLens
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.
Evaluation
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.
Supporting Design
Structural ablations support the design, while input-side masking shows that the ranked components reflect variables with greater influence on prediction.