OBJECTIVE: Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. METHODS: We proposed the use of the Takagi and Sugeno-type "adaptive-network-based fuzzy inference system" (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. RESULTS: Fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB: environment. CONCLUSION: ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting.
OBJECTIVE: Therapeutic drug monitoring (TDM) is a procedure in which the levels of drugs are assayed in various body fluids with the aim of individualizing the dose of critical drugs, such as cyclosporine A. Cyclosporine A assays are performed in blood. METHODS: We proposed the use of the Takagi and Sugeno-type "adaptive-network-based fuzzy inference system" (ANFIS) to predict the concentration of cyclosporine A in blood samples taken from renal transplantation patients. We implemented the ANFIS model using TDM data collected from 138 patients and 20 input parameters. Input parameters for the model consisted of concurrent use of drugs, blood levels, sampling time, age, gender, and dosing intervals. RESULTS: Fuzzy modeling produced eight rules. The developed ANFIS model exhibited a root mean square error (RMSE) of 0.045 with respect to the training data and an error of 0.057 with respect to the checking data in the MATLAB: environment. CONCLUSION: ANFIS can effectively assist physicians in choosing best therapeutic drug dose in the clinical setting.
Authors: T Iwahori; H Takeuchi; N Matsuno; Y Johjima; O Konno; Y Nakamura; K Hama; M Uchiyama; T Ashizawa; K Okuyama; T Nagao; M Abudoshukur; T Hirano; K Oka Journal: Transplant Proc Date: 2005-05 Impact factor: 1.066
Authors: C A Aros; H O Schneider; C A Flores; L G Ardiles; P A Alruiz; V Jerez; S A Mezzano Journal: Transplant Proc Date: 2005-04 Impact factor: 1.066
Authors: G Bertilsson; J Heidrich; K Svensson; M Asman; L Jendeberg; M Sydow-Bäckman; R Ohlsson; H Postlind; P Blomquist; A Berkenstam Journal: Proc Natl Acad Sci U S A Date: 1998-10-13 Impact factor: 11.205