Prediction of single-pool Kt/V based on clinical and hemodialysis variables using multilinear regression, tree-based modeling, and artificial neural networks

Artificial Organs

Volumn 27, Issue 6, 2003, Pages 544-554

  Goldfarb Rumyantzev, Alexander ^a   Schwenk, Michael H ^b   Liu, Samuel ^b   Charytan, Chaim ^b   Spinowitz, Bruce S ^b  

^a UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^b NY Hospital Medical Center of Queens   (United States)

Author keywords

Artificial neural networks; Hemodialysis; Kt V; Linear regression; Prediction; Tree based model; Urea kinetic modeling

Indexed keywords

DIALYSIS; HEMODIALYZERS; METABOLISM; NEURAL NETWORKS; REGRESSION ANALYSIS; STATISTICAL TESTS; UREA;

BLOOD-FLOW RATES; HAEMODIALYSIS; KT/V; MULTILINEAR REGRESSION; REGRESSION TREES; TOTAL NITROGEN; TREE-BASED MODEL; TREES-BASED MODELS; UREA KINETIC MODELING; UREA REDUCTION RATIOS;

FORECASTING;

NITROGEN; UREA;

ALGORITHM; ARTICLE; ARTIFICIAL KIDNEY; ARTIFICIAL NEURAL NETWORK; BLOOD FLOW VELOCITY; BODY HEIGHT; CORRELATION ANALYSIS; CORRELATION COEFFICIENT; FEMALE; HEMODIALYSIS; HUMAN; LINEAR REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; SEX; THEORETICAL MODEL; ULTRAFILTRATION;

EID: 0038518206     PISSN: 0160564X     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.1525-1594.2003.07001.x     Document Type: Article

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