A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning
DOI:
https://doi.org/10.1609/aaai.v37i12.26681Keywords:
GeneralAbstract
Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.Downloads
Published
2023-06-26
How to Cite
Lu, F., Li, W., Zhou, Z., Song, C., Sun, Y., Zhang, Y., Ren, Y., Liao, X., Jin, H., Luo, A., & Zomaya, A. Y. (2023). A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14374-14381. https://doi.org/10.1609/aaai.v37i12.26681
Issue
Section
AAAI Special Track on AI for Social Impact