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Prediction of Serum Creatinine in Hemodialysis Patients Using a Kernel Approach for Longitudinal Data

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Academic year: 2021

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Figure 1.   Framework of data pre-processing. LR: linear regres- regres-sion, LMM: linear mixed-effects model, LS-SVR:  least-squares support vector regression, MLS-SVR:  mixed-effects least-squares support vector regression.
Table 2. Regression coeficient of covariates of fitting the linear  mixed-effects model to serum creatinine
Figure 2. Comparison of predicted and observed values for MLS-SVR and LMM for three patients: (A) patient #1, (B) patient #2, and  (C) patient #3
Figure 3. Variable importance (VIMP) of each factor in prediction of creatinine using MLS-SVR method

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