---
title: "GARLIC：基于图注意力的重症监护多变量时间序列关系学习"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.10969v1"
permalink: "https://feed.astralor.com/items/itm_549f96cefcdad4bd3b0b2589"
published: "2026-08-11T14:29:48.000Z"
score: 71
channel: "tech"
tags: ["GARLIC","可解释深度学习","图注意力网络","缺失数据填补","重症监护"]
---

# GARLIC：基于图注意力的重症监护多变量时间序列关系学习

## AI 点评

> 这篇工作最值得关注的点是把“可解释”从附属品变成了模型结构本身的一部分，而不是事后去解释黑盒。对于做临床预测的团队，尤其是需要给医生合理预警理由的场景，这套思路比单纯刷分更有参考价值。

## 速览

ICU医疗数据多为不规则采样且缺失严重的多变量时间序列，临床预测需兼顾准确与可解释。GARLIC提出可学习指数衰减编码器填补缺失值，用时滞摘要图捕捉传感器间依赖，并以跨维度序列注意力融合全局模式；所有注意力与图边端到端学习，提供观测、信号与边三级解释。通过交替解耦优化稳定训练，在PhysioNet 2012/2019与MIMIC-III上刷新结果，并验证了跨领域泛化能力。

## 关键点

- 提出可学习指数衰减编码器与时间滞后摘要图，端到端学习缺失填补和传感器间依赖。
- 所有注意力权重与图边天然构成观测级、信号级和边级解释，无需事后解释工具。
- 在PhysioNet 2012/2019、MIMIC-III上取得新的SOTA，显著提升AUROC与AUPRC。
- 通过特征移除实验和实时案例验证了重要性归因的保真度与可落地的风险预警能力。

## 全文

Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations. To reconcile auxiliary reconstruction and primary classification objectives, we developed an alternating decoupled optimization scheme that stabilizes training. On three ICU benchmarks (PhysioNet 2012 & 2019, MIMIC-III), GARLIC sets the new state of the art in outcome prediction, significantly improving AUROC and AUPRC over best-performing baselines at comparable computational cost. Ablation studies confirm the contribution of each module, and feature-removal trials validate the fidelity of importance attribution through a monotonic performance drop (full > top 50% > random 50% > bottom 50%). Real-time case studies demonstrate actionable risk warnings with transparent explanations, marking a significant advance toward accurate, explainable deep learning for irregularly sampled ICU time series data. Moreover, we demonstrated \proposed{}'s superiority in data imputation and classification on various time-series datasets beyond the ICU domain, showing its generalizability and applicability to broader tasks.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_549f96cefcdad4bd3b0b2589