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Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation

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  • He, Miao
  • Zhao, Lei
  • Powell, Warren B.

Abstract

In the controlled ovarian hyperstimulation (COH) treatment, clinicians monitor the patients’ physiological responses to gonadotropin administration to tradeoff between pregnancy probability and ovarian hyperstimulation syndrome (OHSS). We formulate the dosage control problem in the COH treatment as a stochastic dynamic program and design approximate dynamic programming (ADP) algorithms to overcome the well-known curses of dimensionality in Markov decision processes (MDP). Our numerical experiments indicate that the piecewise linear (PWL) approximation ADP algorithms can obtain policies that are very close to the one obtained by the MDP benchmark with significantly less solution time.

Suggested Citation

  • He, Miao & Zhao, Lei & Powell, Warren B., 2012. "Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation," European Journal of Operational Research, Elsevier, vol. 222(2), pages 328-340.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:2:p:328-340
    DOI: 10.1016/j.ejor.2012.03.049
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    References listed on IDEAS

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    1. Bahar Biller & Barry L. Nelson, 2005. "Fitting Time-Series Input Processes for Simulation," Operations Research, INFORMS, vol. 53(3), pages 549-559, June.
    2. Miao He & Lei Zhao & Warren Powell, 2010. "Optimal control of dosage decisions in controlled ovarian hyperstimulation," Annals of Operations Research, Springer, vol. 178(1), pages 223-245, July.
    3. Marne C. Cario & Barry L. Nelson, 1998. "Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Processes," INFORMS Journal on Computing, INFORMS, vol. 10(1), pages 72-81, February.
    4. Schmid, Verena, 2012. "Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 219(3), pages 611-621.
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    Cited by:

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    2. Ilya O. Ryzhov & Martijn R. K. Mes & Warren B. Powell & Gerald van den Berg, 2019. "Bayesian Exploration for Approximate Dynamic Programming," Operations Research, INFORMS, vol. 67(1), pages 198-214, January.
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    4. Ulmer, Marlin W. & Thomas, Barrett W., 2020. "Meso-parametric value function approximation for dynamic customer acceptances in delivery routing," European Journal of Operational Research, Elsevier, vol. 285(1), pages 183-195.

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