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Markov Decision Processes for Screening and Treatment of Chronic Diseases

In: Markov Decision Processes in Practice

Author

Listed:
  • Lauren N. Steimle

    (University of Michigan)

  • Brian T. Denton

    (University of Michigan)

Abstract

In recent years, Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) have found important applications to medical decision making in the context of prevention, screening, and treatment of diseases. In this chapter, we provide a review of state-of-the-art models and methods that have been applied to chronic diseases. We provide a tutorial about how to formulate and solve these important problems emphasizing some of the challenges specific to chronic diseases such as diabetes, heart disease, and cancer. Then, we illustrate important considerations for model formulation and solution methods through two examples. The first example is an MDP model for optimal control of drug treatment decisions for managing the risk of heart disease and stroke in patients with type 2 diabetes. The second example is a POMDP model for optimal design of biomarker-based screening policies in the context of prostate cancer. We end the chapter with a discussion of the challenges of using MDPs and POMDPs for medical contexts and describe some important future directions for research.

Suggested Citation

  • Lauren N. Steimle & Brian T. Denton, 2017. "Markov Decision Processes for Screening and Treatment of Chronic Diseases," International Series in Operations Research & Management Science, in: Richard J. Boucherie & Nico M. van Dijk (ed.), Markov Decision Processes in Practice, chapter 0, pages 189-222, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-47766-4_6
    DOI: 10.1007/978-3-319-47766-4_6
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    Citations

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    Cited by:

    1. Lili Wang & Lei Si & Fiona Cocker & Andrew J. Palmer & Kristy Sanderson, 2018. "A Systematic Review of Cost-of-Illness Studies of Multimorbidity," Applied Health Economics and Health Policy, Springer, vol. 16(1), pages 15-29, February.
    2. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    3. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    4. Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.
    5. Zheng Zhang & Brian T. Denton & Todd M. Morgan, 2022. "Optimization of active surveillance strategies for heterogeneous patients with prostate cancer," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4021-4037, November.
    6. Hussein El Hajj & Douglas R. Bish & Ebru K. Bish & Denise M. Kay, 2022. "Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening," Management Science, INFORMS, vol. 68(11), pages 7994-8014, November.
    7. Daniel F. Otero-Leon & Mariel S. Lavieri & Brian T. Denton & Jeremy Sussman & Rodney A. Hayward, 2023. "Monitoring policy in the context of preventive treatment of cardiovascular disease," Health Care Management Science, Springer, vol. 26(1), pages 93-116, March.
    8. Otten, Maarten & Timmer, Judith & Witteveen, Annemieke, 2020. "Stratified breast cancer follow-up using a continuous state partially observable Markov decision process," European Journal of Operational Research, Elsevier, vol. 281(2), pages 464-474.
    9. Anne-France Viet & Stéphane Krebs & Olivier Rat-Aspert & Laurent Jeanpierre & Catherine Belloc & Pauline Ezanno, 2018. "A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.

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