ArticleBioMed research international2021
Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05735288 (Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients), which is not on this map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Pilot-scale, Single-arm, Observational Study to Assess the Utility of a Machine Learning Algorithm in Assessing Fluid Status in Haemodialysis Patients
Who cites it
5 citing papers in PubMed, 21 citations in OpenAlex.
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
- Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile.Heliyon · 2024Article
- Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis.Scientific reports · 2023Article
- Predicting dry weight change in Hemodialysis patients using machine learning.BMC nephrology · 2023Article
- Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.