Chiu JS |
------>authors3_c= ------>paper_class1=1 ------>Impact_Factor=1.342 ------>paper_class3=2 ------>paper_class2=1 ------>vol=25 ------>confirm_bywho=jack ------>insert_bywho=bebble ------>Jurnal_Rank=50.0 ------>authors4_c= ------>comm_author=1 ------>patent_EDate=None ------>authors5_c= ------>publish_day=1 ------>paper_class2Letter=None ------>page2=513 ------>medlineContent= ------>unit=E0700 ------>insert_date=20050729 ------>iam=7 ------>update_date=None ------>author=??? ------>change_event=4 ------>ISSN= ------>authors_c= ------>score=461 ------>journal_name=Am J Nephrol ------>paper_name=Applying artificial neural network to predict total body water in hemodialysis patients ------>confirm_date=20060505 ------>tch_id=084004 ------>pmid=16155360 ------>page1=507 ------>fullAbstract=BACKGROUND: Estimating total body water (TBW) is crucial in determining dry weight and dialytic dose for hemodialysis patients. Several anthropometric equations have been used to predict TBW, but a more accurate method is needed. We developed an artificial neural network (ANN) to predict TBW in hemodialysis patients. METHODS: Demographic data, anthropometric measurements, and multifrequency bioelectrical impedance analysis (MF-BIA) were investigated in 54 patients. TBW measured by MF-BIA (TBW-BIA) was the reference. The predictive value of TBW based on ANN and five anthropometric equations (58% of actual body weight, Watson formula, Hume formula, Chertow formula, and Lee formula) was evaluated. RESULTS: Predictive TBW values derived from anthropometric equations were significantly higher than TBW-BIA (31.341 +/- 6.033 liters). The only non-significant difference was between TBW-ANN (31.468 +/- 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson~s correlation coefficient (0.911) and smallest root mean square error (2.480); its peak centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other five equations. CONCLUSION: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients. ------>tmu_sno=None ------>sno=11621 ------>authors2=Chong CF ------>authors3=Lin YF ------>authors4=Wu CC ------>authors5=Wang YF ------>authors6=Li YC ------>authors6_c= ------>authors=Chiu JS ------>delete_flag=0 ------>SCI_JNo=None ------>authors2_c= ------>publish_area=0 ------>updateTitle=Applying an artificial neural network to predict total body water in hemodialysis patients. ------>language=1 ------>check_flag=None ------>submit_date=None ------>country=None ------>no= ------>patent_SDate=None ------>update_bywho=None ------>publish_year=2005 ------>submit_flag=None ------>publish_month=1 |