Title :
Artificial intelligence approach to determine minimum dose of haemodialysis
Author :
Ray, Monika ; Qidwai, Uvais
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA
Abstract :
Efficiency of haemodialysis in end-stage renal disease (ESRD) is determined by calculating adequacy. The adequacy of dialysis and its measurement have been debated over the past 20 years by authorities concerned about how much of this life-sustaining treatment is appropriate for patients with ESRD. Currently, the minimum dose of dialysis (Kt/V) is assessed by computerised calculation of urea kinetics. Although fairly standard, it is still an approximate method due to the various assumptions made in the development of the final parametric model. In this paper, a new algorithm approach is presented for determining Kt/V using generalised regression neural networks (GRNN) and this research has shown it to be very promising.
Keywords :
artificial intelligence; blood; haemodynamics; neural nets; patient treatment; artificial intelligence; end-stage renal disease; generalised regression neural networks; haemodialysis; life-sustaining treatment; minimum dose of dialysis; urea kinetics; Artificial intelligence; Biomedical monitoring; Blood pressure; Computer science; Diseases; Filters; Kinetic theory; Medical treatment; Pressure control; Wastewater treatment;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223314