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226 results on '"Huo, Shanshan"'

22. The Effectiveness of Electronic Cannulation Atlas in Patients with Arteriovenous Fistula.

Author

Xu, Fang, Shu, Peng, Li, Chenchen, Huo, Shanshan, and Wen, Zhuping

Subjects
ARTERIOVENOUS fistula, JOB satisfaction, HEMODIALYSIS patients, SATISFACTION, CATHETERIZATION
Abstract

This study aimed to assess the effectiveness of an electronic cannulation atlas in preventing and treating complications of arteriovenous fistula. Methods: The observation group, consisting of 92 dialysis patients from July to December 2021, was managed with an electronic cannulation atlas. After 6 months, the incidence of complications such as stenosis, hematoma, thrombus, aneurysm, and cannulation failure was compared between the groups. Nurse satisfaction with the electronic cannulation atlas system was also assessed through a questionnaire. Results: The observation group had lower incidence rates of arteriovenous fistula stenosis, thrombus, aneurysm, and failure rate of cannulation compared to the control group, with statistically significant differences (p< 0.05). The incidence rates of hematoa were similar in both groups, showing no significant difference (p> 0.05). After 3 months of management, the incidence of arteriovenous fistula complications in the observation group was significantly lower than in the control group (p< 0.05). Additionally, utilizing the electronic cannulation atlas system was found to increase nurses' job satisfaction. Conclusion: The use of electronic cannulation atlas for the treatment of patients' arteriovenous fistula could effectively reduce the incidence of complications of patients' arteriovenous fistula, reduce the failure rate of cannulation, reduce the workload of nurses, and improve the job satisfaction of nurses. [ABSTRACT FROM AUTHOR]

Published
2024
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36. An Adaptive Low Computational Cost Alternating Direction Method of Multiplier for RELM Large-Scale Distributed Optimization.

Author

Wang, Ke, Huo, Shanshan, Liu, Banteng, Wang, Zhangquan, and Ren, Tiaojuan

Subjects
*QUASI-Newton methods, *MATRIX inversion, *MACHINE learning, *COST, *CURVATURE
Abstract

In a class of large-scale distributed optimization, the calculation of RELM based on the Moore–Penrose inverse matrix is prohibitively expensive, which hinders the formulation of a computationally efficient optimization model. Attempting to improve the model's convergence performance, this paper proposes a low computing cost Alternating Direction Method of Multipliers (ADMM), where the original update in ADMM is solved inexactly with approximate curvature information. Based on quasi-Newton techniques, the ADMM approach allows us to solve convex optimization with reasonable accuracy and computational effort. By introducing this algorithm into the RELM model, the model fitting problem can be decomposed into a set of subproblems that can be executed in parallel to achieve efficient classification performance. To avoid the storage of expensive Hessian for large problems, BFGS with limited memory is proposed with computational efficiency. And the optimal parameter values of the step-size search method are obtained through Wolfe line search strategy. To demonstrate the superiority of our methods, numerical experiments are conducted on eight real-world datasets. Results on problems arising in machine learning suggest that the proposed method is competitive with other similar methods, both in terms of better computational efficiency as well as accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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