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Guo X, Zhou W, Shi B, Wang X, Du A, Ding Y, Tang J, Guo F. An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200614172536] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Dry Weight (DW) is the lowest weight after dialysis, and patients with
lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches
have been presented to assess the dry weight of hemodialysis patients. However, these traditional
methods all depend on special instruments and professional technicians.
Objective:
In order to avoid this limitation, we need to find a machine-independent way to assess dry
weight, therefore we collected some clinical influencing characteristic data and constructed a
Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.
Methods::
In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements,
and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body
Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were
closely related to their dry weight. All these relevant data were used to enter the regression equation.
Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS)
model was proposed to predict the dry weight of hemodialysis patients.
Result:
The experimental results show that dry weight is positively correlated with BMI and HR.
And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively
correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was
1.3817.
Conclusion:
Our proposed model could serve as a viable alternative for dry weight estimation of
hemodialysis patients, thus providing a new way for clinical practice. Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients,
thus providing a new way for the clinic.
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Affiliation(s)
- Xiaoyi Guo
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Wei Zhou
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Bin Shi
- Hemodialysis Center, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Xiaohua Wang
- Department of Urology, the First Affiliated Hospital of Soochow University, 215006, Suzhou, China
| | - Aiyan Du
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L 2,1-Norm. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6627650. [PMID: 33628794 PMCID: PMC7880720 DOI: 10.1155/2021/6627650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 11/28/2022]
Abstract
Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient's dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.
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Yoowannakul S, Kotecha T, Fontana M, Davenport A. Do Pre‐Hemodialysis Estimates of Extracellular Volume Excess Using Bioimpedance and N‐Terminal Brain Natriuretic Peptide Correlate With Cardiac Chamber Size Measured by Magnetic Resonance Imaging? Ther Apher Dial 2018; 23:362-368. [DOI: 10.1111/1744-9987.12779] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 11/20/2018] [Accepted: 11/20/2018] [Indexed: 01/01/2023]
Affiliation(s)
| | - Tushar Kotecha
- National Amyloid Centre, Royal Free Hospital, Department of MedicineUniversity College London London UK
- Department of CardiologyRoyal Free Hospital London UK
| | - Marianna Fontana
- National Amyloid Centre, Royal Free Hospital, Department of MedicineUniversity College London London UK
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