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.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Ou, Y., & Li, W. (2026). SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning. https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning
MLA 9
Ou, Yikun, and Wei Li. "SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning." https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning.
Chicago (author–date)
Ou, Yikun, and Wei Li. 2026. "SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning." https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning.
Harvard
Ou, Y. and Li, W. (2026) 'SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning', Available at: https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning.
Vancouver
Ou Y, Li W. SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning. https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning
IEEE
Y. Ou, and W. Li, "SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning," https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning.