Abstract
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.
Highlights
Dry weight (DW) refers to a patient’s target weight after the end of dialysis (Grassmann et al, 2000; Wabel et al, 2009)
We proposed a novel predictive model based on radial basis function neural network (RBFN)
The data set of this study came from the hemodialysis center of Wuxi and the northern Jiangsu People’s Hospital
Summary
Dry weight (DW) refers to a patient’s target weight after the end of dialysis (Grassmann et al, 2000; Wabel et al, 2009). After removing excess water from the body, the patient had no facial swelling, wheezing or sitting breathing, edema of both lower limbs, and distended jugular vein (Alexiadis et al, 2016). The patient’s blood pressure, heart rate, breathing, and other vital signs are stable. Good dry weight control can effectively reduce adverse reactions during dialysis. The DW of hemodialysis patients is mainly evaluated by clinical means. This method is labor-intensive and time-consuming, and requires repeated use of various clinical instruments and biological indicators to complete the evaluation. In the past 10 years, a measuring instrument based on human bioelectrical impedance analysis
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