Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks

IEEE Transactions on Biomedical Engineering

Volumn 50, Issue 4, 2003, Pages 442-448

  Camps Valls, Gustavo ^a   Porta Oltra, Begoña ^b   Soria Olivas, Emilio ^a   Martín Guerrero, José David ^a   Serrano López, Antonio José ^a   Pérez Ruixo, Juan José ^b,c   Jiménez Torres, Nicolás Víctor ^a,b  

^a UNIVERSITY OF VALENCIA   (Spain)

^b HOSPITAL UNIVERSITARIO DR PESET   (Spain)

^c JANSSEN RESEARCH AND DEVELOPMENT   (Belgium)

Author keywords

Blood concentration; Clinical pharmacokinetics; Cyclosporine; Drug monitoring; Kidney transplantation; Neural networks; Time series prediction

Indexed keywords

BLOOD; DECISION MAKING; NEURAL NETWORKS; STATISTICAL METHODS;

KIDNEY TRANSPLANTATION;

DRUG DOSAGE;

CYCLOSPORIN A; CYCLOSPORIN; DRUG DERIVATIVE; MYCOPHENOLIC ACID; MYCOPHENOLIC ACID 2 MORPHOLINOETHYL ESTER; PREDNISONE;

ACCURACY; ADULT; AGED; ANALYTIC METHOD; ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; DECISION MAKING; DOSE CALCULATION; DRUG BLOOD LEVEL; DRUG MONITORING; FEMALE; HUMAN; KIDNEY TRANSPLANTATION; MALE; MATHEMATICAL MODEL; NERVE CELL NETWORK; PREDICTION; STATISTICAL ANALYSIS; STEADY STATE; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BLOOD; CLINICAL TRIAL; COMPARATIVE STUDY; COMPUTER ASSISTED DRUG THERAPY; CONTROLLED CLINICAL TRIAL; DRUG ADMINISTRATION; DRUG COMBINATION; GRAFT REJECTION; METHODOLOGY; ORAL DRUG ADMINISTRATION; PREDICTION AND FORECASTING; RANDOMIZED CONTROLLED TRIAL; STATISTICS; VALIDATION STUDY;

ADMINISTRATION, ORAL; ALGORITHMS; CYCLOSPORINE; DRUG ADMINISTRATION SCHEDULE; DRUG THERAPY, COMBINATION; DRUG THERAPY, COMPUTER-ASSISTED; GRAFT REJECTION; KIDNEY TRANSPLANTATION; MODELS, BIOLOGICAL; MODELS, CARDIOVASCULAR; MYCOPHENOLIC ACID; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PREDNISONE; STATISTICS;

EID: 0242285721     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2003.809498     Document Type: Article

References (18)

1. P. Belitsky, "Neoral used in the renal transplant recipient," Transplantation Proc., vol. 32, pp. S10-S19, May 2000.
2. A. Lindholm, "Factors influencing the pharmacokinetics of cyclosporine in man," Therapeutic Drug Monitoring, vol. 13, pp. 465-477, Nov. 1991.
3. C. Campana, M. B. Regazzi, and I. Buggia, "Clinically significant drug interactions with ciclosporin," Clin. Pharmacokinetics, vol. 30, pp. 141-179, Feb. 1996.
4. G. Levy, P. Burra, A. Cavallari, C. Duvoux, J. Lake, and A. D. Mayer, "Improved clinical outcomes for liver transplant recipients using cyclosporine monitoring based on 2-hr post-dose levels (c2)," Transplantation, vol. 73, p. 953, Mar. 2002.
5. M. E. Brier, "Empirical pharmacokinetic predictions for cyclosporine using a time series neural network," Pharmaceutical Res., vol. 12, no. S363, 1995.
6. A. S. Hussain, R. D. Johnson, N. N. Vachharajani, and W. A. Ritschel, "Feasibility of developing a neural network for prediction of human pharmacokinetic parameters from animal data," Pharmaceutical Res., vol. 10, pp. 466-469, Mar. 1993.
7. P. Veng-Pedersen and N. Modi, "Application of neural networks to pharmacodynamics," J. Pharmaceutical Sci., vol. 82, pp. 918-926, 1993.
8. M. Oellerich, V. W. Armstrong, B. Kahan, L. Shaw, D. W. Holt, R. Yatscoff, A. Lindholm, P. Halloran, K. Gallicano, and K. Wonigeit, "Lake Louise consensus conference on cyclosporin monitoring in organ transplantation: Report of the consensus panel," Therapeutic Drug Monitoring, vol. 17, pp. 642-654, Dec. 1995.
9. T. A. S. Assays, "Manual analitique," Laboratories ABBOTT, Division Diagnostic, Rundix, France, XII-CYCLO-MONO-13.
10. G. Camps, E. Soria, B. Porta, J. J. Ruixo, J. D. Martín, A. J. Serrano, and N. V. Jiménez, "A neural approach to ciclosporine dose prediction," presented at the World Cong. Medical Physics and Biomedical Engineering, Chicago, IL, July 2000.
11. A. S. Weigend and N. A. Gershenfeld, "Time series prediction. Forecasting the future and understanding the past," in Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis held in Santa Fe, New Mexico, May 14-17, 1992. Proceedings Volume XV. Reading, MA: Addison-Wesley, 1994, vol. XV.
12. S. Haykin, Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice Hall, 1999.
13. E. A. Wan, "Finite impulse response neural networks with applications in time series prediction," Ph.D. dissertation, Dept. Elect. Eng., Stanford Univ., Stanford, CA, Nov. 1993.
14. J. L. Elman, "Finding structure in time," Cogn. Sci., vol. 14, pp. 179-211, 1988.
15. R. Clemen, "Combining forecasts: A review and annotated bibliography," Int. J. Forecasting, vol. 5, pp. 559-583, 1989.
16. S. Hashem, "Optimal linear combinations of neural networks," Neural Networks, vol. 10, no. 4, pp. 599-614, 1997.
17. L. Ljung, System Identification. Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1987.
18. G. Camps-Valls, E. Soria, J. D. Martín, and J. J. Jiménez, "Neural networks ensemble for cyclosporine concentration monitoring," in Lecture Notes in Computer Science. Berlin, Germany: Springer-Verlag, Aug. 2001, International Conference on Artificial Neural Networks, pp. 706-711.