Enhancing Hypotension Prediction in Real-Time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66538-7_5
Reference12 articles.
1. Kim, Y., Seo, J., O’Reilly, U.M.: Large-scale methodological comparison of acute hypotensive episode forecasting using MIMIC2 physiological waveforms. In: IEEE 27th International Symposium on Computer-Based Medical Systems. New York, NY (2014)
2. Zhang, G., Yuan, J., Yu, M., Wu, T., Luo, X., Chen, F.: A machine learning method for acute hypotensive episodes prediction using only non-invasive parameters. Comput. Methods Prog. Biomed. 200, 105845 (2021)
3. Afsar F. Prediction of acute hypotension episodes in patients taking pressor medication using modeling of arterial blood pressure waveforms. In: 4th International Conference on Bioinformatics and Biomedical Engineering. Chengdu (2010)
4. Lee, J., Woo, J., Kang, A.R., Jeong, Y.S., Jung, W., Lee, M., et al.: Comparative analysis on machine learning and deep learning to predict post-induction hypotension. Sensors. 20, 4575 (2020)
5. Eldele, E., et al.: Time-series representation learning via temporal and contextual contrasting. arXiv preprint arXiv:2106.14112 (2021)
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