SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection
This work addresses the detection of PVA for mechanically ventilated patients, offering an interpretable method that is incremental over existing computational approaches.
The authors tackled the problem of detecting patient-ventilator asynchrony (PVA), a critical issue affecting up to 85% of patients, by proposing a shapelet-based approach called SHIP, which significantly improved detection accuracy and provided interpretable insights.
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.