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arXiv AI By Chenyang Wang, Jiawei Theresa Tang, Xinran Liu, Xinyu Lu, Xulei Yang

NECA: Neighbor Event Curve Augmentation for Multi-Horizon ICU Deterioration Prediction

Read the original on arXiv AI →

The paper introduces NECA, a model‑agnostic augmentation technique that retrieves similar patients from the training set and transforms their organ‑dysfunction event onset times into cumulative event‑curve features for multiple future horizons (6h, 12h, 24h, 42h). These features are concatenated with the original patient representation and fed to standard downstream predictors, leading to improved PR‑AUC and ROC‑AUC on PhysioNet 2012 and a MIMIC‑IV subset across several organ‑dysfunction tasks. Ablation studies show that clinically meaningful retrieval and horizon‑specific neighbor risk are the main drivers of performance gains, especially for neurological dysfunction events.

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 AI.

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