用形状片段提升呼吸机不同步检测的准确率与可解释性
SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection
- 基于时间序列形状片段提取关键特征，增强模型判别力
- 在真实医疗数据上实现显著更高的检测准确率
- 适合临床医生理解模型决策，适用于重症监护场景
患者-呼吸机不同步（PVA）是机械通气中常见且严重的问题，影响高达85%的患者，可能导致不适、睡眠障碍，甚至引发呼吸机诱发肺损伤和膈肌功能障碍。传统依赖医护人员手动调整的管理模式常因延迟和误判而效果不佳。尽管已有基于规则、统计或深度学习的计算方法，但普遍存在数据不平衡和缺乏可解释性问题。本文提出基于形状片段的SHIP方法，利用时间序列中具有判别性的子序列（即形状片段）来提升检测精度与可解释性。通过形状片段增强数据分布并构建形状片段池，对原始数据进行重构以支持更有效的分类。结合形状片段与统计特征，在分类器中识别PVA事件。实验结果表明，该方法在真实医疗数据集上显著提升了检测性能，并为模型决策提供了可解释性洞察。
原文摘要 · Abstract (English)
Patient-ventilator asynchrony (PVA) is a common and critical issue during mechanical ventilation, affecting up to 85% of patients. PVA can result in clinical complications such as discomfort, sleep disruption, and potentially more severe conditions like ventilator-induced lung injury and diaphragm dysfunction. Traditional PVA management, which relies on manual adjustments by healthcare providers, is often inadequate due to delays and errors. While various computational methods, including rule-based, statistical, and deep learning approaches, have been developed to detect PVA events, they face challenges related to dataset imbalances and lack of interpretability. In this work, we propose a shapelet-based approach SHIP for PVA detection, utilizing shapelets - discriminative subsequences in time-series data - to enhance detection accuracy and interpretability. Our method addresses dataset imbalances through shapelet-based data augmentation and constructs a shapelet pool to transform the dataset for more effective classification. The combined shapelet and statistical features are then used in a classifier to identify PVA events. Experimental results on medical datasets show that SHIP significantly improves PVA detection while providing interpretable insights into model decisions.
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