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Comparison of Some Suboptimal Control Policies in Medical Drug Therapy

Author

Listed:
  • Chuanpu Hu

    (Stanford University, Stanford, California)

  • William S. Lovejoy

    (Stanford University, Stanford, California)

  • Steven L. Shafer

    (Stanford University, Stanford, California)

Abstract

In drug therapy, efficient dosage policies are needed to maintain drug concentrations at target. The relationship between the concentration of a drug and the dosages is often described by compartment models in which the parameters are unknown, although prior knowledge may be available and can be updated after blood samples are taken during the therapy. In this paper we define some tractable policies for adaptive control of drug concentrations in compartment models and compare their performances using computer simulation in a one-compartment model. We also discuss the effects of assuming normal priors, discrete approximation of a continuous prior, using nonquadratic costs, and information probing. From the simulation we derive intuition as to what types of policies perform well and address the topic of actively versus passively learning.

Suggested Citation

  • Chuanpu Hu & William S. Lovejoy & Steven L. Shafer, 1996. "Comparison of Some Suboptimal Control Policies in Medical Drug Therapy," Operations Research, INFORMS, vol. 44(5), pages 696-709, October.
    • Handle: RePEc:inm:oropre:v:44:y:1996:i:5:p:696-709
    DOI: 10.1287/opre.44.5.696
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    Citations

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    Cited by:

    1. James T. Treharne & Charles R. Sox, 2002. "Adaptive Inventory Control for Nonstationary Demand and Partial Information," Management Science, INFORMS, vol. 48(5), pages 607-624, May.
    2. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
    3. Oguzhan Alagoz & Heather Hsu & Andrew J. Schaefer & Mark S. Roberts, 2010. "Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty," Medical Decision Making, , vol. 30(4), pages 474-483, July.
    4. Alexandre X. Carvalho & Martin L. Puterman, 2005. "Dynamic Optimization and Learning: How Should a Manager set Prices when the Demand Function is Unknown ?," Discussion Papers 1117, Instituto de Pesquisa Econômica Aplicada - IPEA.
    5. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    6. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
    7. Alexandre X. Carvalho & Martin L. Puterman, 2015. "Dynamic Optimization and Learning: How Should a Manager Set Prices When the Demand Function is Unknown?," Discussion Papers 0158, Instituto de Pesquisa Econômica Aplicada - IPEA.
    8. Huiqiao Su & Guohua Wan & Shan Wang, 2019. "Online scheduling for outpatient services with heterogeneous patients and physicians," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 123-149, January.
    9. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
    10. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    11. Kotas, Jakob & Ghate, Archis, 2018. "Bayesian learning of dose–response parameters from a cohort under response-guided dosing," European Journal of Operational Research, Elsevier, vol. 265(1), pages 328-343.

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