A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients

Expert Systems with Applications

Volumn 36, Issue 6, 2009, Pages 9737-9742

  Martín Guerrero, José D ^a   Gomez, Faustino ^b   Soria Olivas, Emilio ^a   Schmidhuber, Jürgen ^b   Climente Martí, Mónica ^c   Jiménez Torres, N Víctor ^a,c  

^a UNIVERSITY OF VALENCIA   (Spain)

^b DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

^c University Hospital   (Spain)

Author keywords

Anemia; Chronic renal failure; Clinical pharmacokinetics; Erythropoietin; Reinforcement learning

Indexed keywords

ANEMIA; CHRONIC RENAL FAILURE; CLINICAL PHARMACOKINETICS; ECONOMIC SAVINGS; ERYTHROPOIETIN; FUNCTION APPROXIMATOR; HEALTH CARE SYSTEMS; HEMODIALYSIS PATIENTS; HOSPITAL POLICIES; MEDICAL PROTOCOLS; MULTI-LAYER PERCEPTRON; Q-LEARNING ALGORITHMS; QUALITY OF LIVES; REINFORCEMENT LEARNING APPROACHES; SIDE-EFFECTS;

DIALYSIS; DRUG DOSAGE; DRUG PRODUCTS; EDUCATION; HEALTH CARE; LEARNING ALGORITHMS; NEURAL NETWORKS; PHARMACOKINETICS; REINFORCEMENT LEARNING;

REINFORCEMENT;

EID: 64049101720     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.02.041     Document Type: Article

References (13)

1. Bellazzi R. Drug delivery optimization through bayesian networks: An application to erythropoietin therapy in uremic anemia. Computers and Biomedical Research 26 (1992) 274-293
2. Bellazzi R., Siviero C., and Bellazzi R. Mathematical modeling of erythropoietin therapy in uremic anemia. Does it improve cost-effectiveness?. Haematologica 79 (1994) 154-164
3. Bertsekas, D., Borkar, V., & Nedi, A. (2003). Improved temporal difference methods with linear function approximation. Tech. Rep. LIDS-P-2573, MIT.
4. Bertsekas D., and Tsitsiklis J. Neuro-dynamic programming (1996), Athenas Scientific, Beltmont, MA, USA
5. Cohen, H., Dowling, T., & Goodin, S. (2005). Anemia in cancer and chronic kidney disease: Clinical and financial considerations. Tech. Rep., American Society of Health-System Pharmacists.
6. Gaweda A.E., Muezzinoglu M.K., Aronoff G.R., Jacobs A.A., Zurada J.M., and Brier M. Individualization of pharmacological anemia management using reinforcement learning. Neural Networks 18 (2005) 826-834
7. Jacobs A.A., Lada P., Zurada J.M., Brier M.E., and Aronoff G. Predictors of hematocrit in hemodialysis patients as determined by artificial neural networks. Journal of American Nephrology 12 (2001) 387A
8. Lynne Peterson, L. (2004). FDA Oncologic Drugs Advisory Committee (ODAC) meeting on the safety of erythropoietin in oncology. Trends in medicine (pp. 1-4).
9. Martín J.D., Camps G., Soria E., Serrano A.J., Pérez J.J., and Jiménez N.V. Dosage individualization of erythropoietin using a profile-dependent support vector regression. IEEE Transactions on Biomedical Engineering 50 10 (2003) 1136-1142
10. Martín J.D., Soria E., Camps G., Serrano A.J., Pérez J.J., and Jiménez N.V. Use of neural networks for dosage individualisation of erythropoietin in patients with secondary anemia to chronic renal failure. Computers in Biology and Medicine 33 4 (2003) 361-373
11. Steensma D., Molina R., Sloan J., Nikcevich D., Schaefer P., Rowland K.J., et al. Phase III study of two different dosing schedules of erythropoietin in anemic patients with cancer. Journal of Clinical Oncology 24 7 (2006) 1079-1089
12. Sutton R., and Barto A. Reinforcement learning: An introduction (1998), MIT Press, Cambridge, MA, USA
13. Werbos, P. J. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. Thesis. Department of Applied Mathematics, Harvard University, Cambridge, MA, USA.