Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease
Copyright © 2021 Guo, Zhou, Yu, Cai, Zhang, Du, Lu, Ding and Li..
Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Frontiers in physiology - 12(2021) vom: 01., Seite 790086 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Xiaoyi [VerfasserIn] |
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| Links: |
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| Themen: |
Dry weight |
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| Anmerkungen: |
Date Revised 31.12.2021 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fphys.2021.790086 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM335021069 |
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| 520 | |a Copyright © 2021 Guo, Zhou, Yu, Cai, Zhang, Du, Lu, Ding and Li. | ||
| 520 | |a Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a RBF networks | |
| 650 | 4 | |a dry weight | |
| 650 | 4 | |a end-stage renal disease | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a multiple Laplacian regularized model | |
| 700 | 1 | |a Zhou, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Yinghua |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Aiyan |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Qun |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Yijie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chao |e verfasserin |4 aut | |
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