用多视角增强学习，20%监测时间就提前预测败血症
MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction
- 构建多内源视角特征，捕捉微弱早期时序信号
- 仅需20%监测时间，达到顶尖模型的预测准确率
- 适合重症监护室早期预警，提升抢救时机
败血症是重症监护病房（ICUs）中致死率极高的感染性综合征。早期精准预测对及时干预至关重要，但因早期症状细微且死亡率迅速上升，仍具挑战。尽管人工智能提升了预测效率，现有方法难以捕捉微弱的早期时序信号。本文提出多内源视图表征增强（MERE）机制，构建丰富特征视图，并结合级联双卷积时序注意力（CDTA）模块实现多尺度时序表示学习。所提MEET-Sepsis框架仅需标准SOTA方法20%的监测时间即可达成竞争性预测精度，显著推进早期败血症预测。大量实验验证了其有效性。代码已公开：https://github.com/yueliangy/MEET-Sepsis。
原文摘要 · Abstract (English)
Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely intervention, yet remains challenging due to subtle early manifestations and rapidly escalating mortality. While AI has improved SP efficiency, existing methods struggle to capture weak early temporal signals. This paper introduces a Multi-Endogenous-view Representation Enhancement (MERE) mechanism to construct enriched feature views, coupled with a Cascaded Dual-convolution Time-series Attention (CDTA) module for multi-scale temporal representation learning. The proposed MEET-Sepsis framework achieves competitive prediction accuracy using only 20% of the ICU monitoring time required by SOTA methods, significantly advancing early SP. Extensive validation confirms its efficacy. Code is available at: https://github.com/yueliangy/MEET-Sepsis.
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