IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v44y1996i5p696-709.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.44.5.696
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.44.5.696?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    7. 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.
    8. 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.
    9. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    10. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:44:y:1996:i:5:p:696-709. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.