1. Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.
- Author
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Do TC, Yang HJ, Kim SH, Kho BG, and Park JK
- Abstract
Objective: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end deep learning architecture that interpolates high-dimensional sequential data for the early detection of clinical deterioration events., Materials and Methods: We consider the problem of detecting deterioration events with two stages: predicting the "detection" status, a pre-event state; and predicting the event from detection time. Our approach involves the development of dual-channel graph attention networks with multi-task learning strategy by jointly learning task relatedness with a shared model for multiple prediction in multivariate time-series., Results: The experiments are conducted on two clinical time-series datasets collected from intensive care units (ICUs). Our model has shown the potential performance compared to other state-of-the-art methods, in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC)., Discussion: The proposed dual-channel graph attention networks can explicitly learn the correlations in both features and time domains of multivariate time-series. Our proposed objective function also can handle the problems of learning task relations and reducing task imbalance effects in multi-task learning., Conclusion: Applying our proposed framework architecture could facilitate the implementation of early detecting in-hospital deterioration events., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
- Published
- 2024
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