IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v17y2014i3p270-283.html
   My bibliography  Save this article

A decision-making framework for adaptive pain management

Author

Listed:
  • Ching-Feng Lin
  • Aera LeBoulluec
  • Li Zeng
  • Victoria Chen
  • Robert Gatchel

Abstract

Pain management is a critical international health issue. The Eugene McDermott Center for Pain Management at The University of Texas Southwestern Medical Center conducted a two-stage interdisciplinary pain management program that considers a wide variety of treatments. Prior to treatment (beginning of Stage 1), an evaluation records the patient’s pain characteristics, medical history and related health parameters. A treatment regime is then determined. At the midpoint of the program (beginning of Stage 2), an evaluation is conducted to determine if an adjustment in the treatment should be made. A final evaluation is conducted at the end of the program to assess final outcomes. We structure this decision-making process using dynamic programming (DP) to generate adaptive treatment strategies for this two-stage program. An approximate DP solution method is employed in which state transition models are constructed empirically based on data from the pain management program, and the future value function is approximated using state space discretization based on a Latin hypercube design and artificial neural networks. The optimization seeks for treatment plans that minimize treatment dosage and pain levels simultaneously. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Ching-Feng Lin & Aera LeBoulluec & Li Zeng & Victoria Chen & Robert Gatchel, 2014. "A decision-making framework for adaptive pain management," Health Care Management Science, Springer, vol. 17(3), pages 270-283, September.
    • Handle: RePEc:kap:hcarem:v:17:y:2014:i:3:p:270-283
    DOI: 10.1007/s10729-013-9252-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10729-013-9252-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10729-013-9252-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    2. Victoria C. P. Chen & David Ruppert & Christine A. Shoemaker, 1999. "Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming," Operations Research, INFORMS, vol. 47(1), pages 38-53, February.
    3. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Abdullah Gökçınar & Metin Çakanyıldırım & Theodore Price & Meredith C. B. Adams, 2022. "Balanced Opioid Prescribing via a Clinical Trade-Off: Pain Relief vs. Adverse Effects of Discomfort, Dependence, and Tolerance/Hypersensitivity," Decision Analysis, INFORMS, vol. 19(4), pages 297-318, December.
    2. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    2. Naumzik, Christof & Feuerriegel, Stefan & Nielsen, Anne Molgaard, 2023. "Data-driven dynamic treatment planning for chronic diseases," European Journal of Operational Research, Elsevier, vol. 305(2), pages 853-867.
    3. 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.
    4. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    5. Jin Wang & Donglin Zeng & D. Y. Lin, 2022. "Semiparametric single-index models for optimal treatment regimens with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 744-763, October.
    6. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    7. Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    8. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List," Operations Research, INFORMS, vol. 55(1), pages 24-36, February.
    9. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    10. Eike Nohdurft & Elisa Long & Stefan Spinler, 2017. "Was Angelina Jolie Right? Optimizing Cancer Prevention Strategies Among BRCA Mutation Carriers," Decision Analysis, INFORMS, vol. 14(3), pages 139-169, September.
    11. Han, Sukjin, 2021. "Identification in nonparametric models for dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 132-147.
    12. Mason, J.E. & Denton, B.T. & Shah, N.D. & Smith, S.A., 2014. "Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 727-738.
    13. Satır, Benhür & Erenay, Fatih Safa & Bookbinder, James H., 2018. "Shipment consolidation with two demand classes: Rationing the dispatch capacity," European Journal of Operational Research, Elsevier, vol. 270(1), pages 171-184.
    14. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2016. "Model uncertainty and the effect of shall-issue right-to-carry laws on crime," European Economic Review, Elsevier, vol. 81(C), pages 32-67.
    15. Kastoryano, Stephen, 2024. "Biological, Behavioural and Spurious Selection on the Kidney Transplant Waitlist," IZA Discussion Papers 16995, Institute of Labor Economics (IZA).
    16. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    17. Zhao, Xian & He, Zongda & Wu, Yaguang & Qiu, Qingan, 2022. "Joint optimization of condition-based performance control and maintenance policies for mission-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    18. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2013. "Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 380-416, February.
    19. Yufan Zhao & Donglin Zeng & Mark A. Socinski & Michael R. Kosorok, 2011. "Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer," Biometrics, The International Biometric Society, vol. 67(4), pages 1422-1433, December.
    20. Luo, Yu & Graham, Daniel J. & McCoy, Emma J., 2023. "Semiparametric Bayesian doubly robust causal estimation," LSE Research Online Documents on Economics 117944, London School of Economics and Political Science, LSE Library.

    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:kap:hcarem:v:17:y:2014:i:3:p:270-283. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.