Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

Guo, Xiaoyi; Zhou, Wei; Lü, Qun; Du, Aiyan; Cai, Yinghua; Ding, Yongsheng

doi:10.1155/2021/6627650

Cited by 13 publications

(8 citation statements)

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“…Experiments for the rolling bearing fault diagnosis process demonstrate that the DM-RVFLNN model is superior and effective compared to standard RVFL. Considering the topological relationship of samples and to improve the robustness of the RVFL model, sparse Laplacian regularized RVFL neural network with L 2,1 -norm (SLapRVFL) [60] was proposed to assess the dry weight of hemodialysis patients and the experiments demonstrate that SLapRVFL is more robust than standard RVFL. Parija et al [61] proposed minimum variance-based kernel RVFL (MVKRVFL) to identify the seizure and non-seizure epileptic EEG signal.…”

Section: Rvfl With Manifold Learning Theorymentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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Section: Rvfl With Manifold Learning Theorymentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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show abstract

“…60,61 There has been significant progress in predicting complex volume-related events during dialysis using machine learning 56,64,65 (Table 3). Machine learning has been applied to the assessment of dry weight in dialysis patients using small dialysis datasets, [66][67][68][69][70] as well in the prediction of volume-related adverse events 56,64,65 (Table 3). Barbieri et al created a prototype algorithm for the prediction of intradialytic hypotension (IDH) using an artificial neural network consisting of 60 variables to create a multiple endpoint model predicting sessionspecific Kt/V, fluid volume removal, HR, and blood pressure (BP).…”

Section: Multiparameter Biosensors In Heart Failurementioning

confidence: 99%

“…Gou et al targeted clinical dry weight prediction using data from 476 HD patients augmented with 10 cross‐fold validation 66 . A Sparse Laplacian regularized Random Vector Functional Link model consisting of seven input features (age, gender, systolic blood pressure, diastolic blood pressure, BMI, heart rate, and years on dialysis) was found to have the best performance in predicting clinical dry weight ( R 2 = 0.9501, RMSE 1.3136 kg), in comparison to Multiple Kernel Support Vector Regression, Multikernel Ridge Regression, Linear Regression, an Artificial Neural Network, and bioimpedance using BCM 66 . This work was built upon in a subsequent study that examined the use of a multiple Laplacian‐regularized radial basis function neural network model in the same cohort of 476 patients.…”

Section: Introductionmentioning

confidence: 99%

“… 66 A Sparse Laplacian regularized Random Vector Functional Link model consisting of seven input features (age, gender, systolic blood pressure, diastolic blood pressure, BMI, heart rate, and years on dialysis) was found to have the best performance in predicting clinical dry weight ( R 2 = 0.9501, RMSE 1.3136 kg), in comparison to Multiple Kernel Support Vector Regression, Multikernel Ridge Regression, Linear Regression, an Artificial Neural Network, and bioimpedance using BCM. 66 This work was built upon in a subsequent study that examined the use of a multiple Laplacian‐regularized radial basis function neural network model in the same cohort of 476 patients. The model had an excellent correlation with clinical dry weight ( R = 0.95), and demonstrated a ratio‐out‐of‐agreement of <5% in a Bland–Altman analysis, suggesting that it could be interchanged with clinical dry weight.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence and digital health for volume maintenance in hemodialysis patients

Sandys

Sexton

O’Seaghdha

2022

Hemodialysis International

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Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.

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“…Olivier Niel et al found that their ML model was able to predict C-DW with good agreement, with a mean difference of 0.09% of C-DW. Although, there was a wide limit of agreement at -4.27 to +4.44% of C-DW (absolute kilogram was not reported), and the number out of agreement was 20 out of 476 samples 41 . The input parameters in this study included demographic data, and time-variable data as a time stamp of pre-dialysis blood pressure and heart rate was used as an input.…”

Section: Machine Learning Model and Dry Weight Predictionmentioning

confidence: 92%

Development of an artificial intelligence model for prediction of dry weight in chronic hemodialysis patients and assessment of its accuracy compared to standard bioelectrical impedance analysis

Boonvisuth

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Proper determination of dry weight (DW) is crucial for achieving positive outcomes in hemodialysis (HD) patients. However, the traditional clinical assessment of DW (C-DW) is often inaccurate. Recently, bioimpedance spectroscopy (BIS) analysis using a Body Composition Monitor (BCM) device has emerged as a gold standard method for determining DW (BCM-DW). Despite its accuracy, the high cost of the BCM device limits its accessibility. To overcome this challenge, the current study proposes a machine learning (ML) model, which is a part of artificial intelligence (AI), to assess DW using available clinical and laboratory parameters. Objective: To develop an ML model for predicting DW (ML-DW) and compare it with BCM-DW. Methods: The study consisted of a model development phase and a performance assessment phase. Retrospective data from chronic HD patients between 2017 and 2022 from two dialysis centers in Bangkok were retrieved. The parameters for this ML model included demographic, dialysis prescription, laboratory, and intradialytic time-varying data. The data utilized during the ML model development phase consisted of a training group for optimizing the parameters of the models and a validation group for determining when to stop the optimization. The final output of the model was ML-DW. The primary outcome of the study was the agreement comparison between ML-DW and BCM-DW. Results: All 56,000 time-varying data from 1,151 HD sessions were included in the ML model. The mean BCM-DW was 58.8?11.7 kgs, while the mean predicted ML-DW from the model was 59.5?11.3 kgs. The Bland-Altman plot showed the bias estimated by the mean difference was 0.78 kg, and the limit of agreement was -3.7 to 2.2 kg. Conclusion: This was the first study that developed a machine learning model aimed at predicting BCM-DW. Compared to other models, this one tried to explore the utilization of time-series data in the input variables. It also demonstrated external validation across different institutions. This study served as a proof-of-concept that machine learning can be a useful tool for DW prediction, but it is not yet a replacement tool for BCM. This warrants further model development that can be widely used in real practice.

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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

Cited by 13 publications

References 56 publications

Random vector functional link network: recent developments, applications, and future directions

Random vector functional link network: recent developments, applications, and future directions

Artificial intelligence and digital health for volume maintenance in hemodialysis patients

Development of an artificial intelligence model for prediction of dry weight in chronic hemodialysis patients and assessment of its accuracy compared to standard bioelectrical impedance analysis

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