[
    {
        "id": "osp-16865",
        "type": "article-journal",
        "title": "SepsisLens: Structure-Preserving Sequence Modelling for Decomposable Early Sepsis Warning",
        "author": [
            {
                "family": "Ou",
                "given": "Yikun"
            },
            {
                "family": "Li",
                "given": "Wei"
            }
        ],
        "URL": "https://omanscience.com/en/articles/sepsislens-structure-preserving-sequence-modelling-for-decomposable-early-sepsis-warning",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]