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A Simulation Optimization Approach forPrecision Medicine

In: AI and Analytics for Public Health

Author

Listed:
  • Jianzhong Du

    (City University of Hong Kong)

  • Siyang Gao

    (City University of Hong Kong
    City University of Hong Kong)

  • Chun-Hung Chen

    (George Mason University)

Abstract

In this research, we consider the emerging problem of precision medicine (PM) in healthcare. We use the tool of simulation to evaluate the performance of feasible treatment methods and make tailored treatment decision for the patients. While simulation enables us to model complex, personalized, and stochastic behaviours, efficiency is still a big concern. To address the computational challenge of conducting simulation experiments, we formulate the PM problem into Ranking and Selection in the presence of covariates and propose an efficient and simple algorithm that can be proven to achieve the optimal allocation for PM asymptotically. A PM case study built from real-world data in the literature shows when compared with the traditional practice for solving PM problems by simulation, the new algorithm can significantly save computational resources.

Suggested Citation

  • Jianzhong Du & Siyang Gao & Chun-Hung Chen, 2022. "A Simulation Optimization Approach forPrecision Medicine," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 281-289, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_20
    DOI: 10.1007/978-3-030-75166-1_20
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    Cited by:

    1. Torra, Vicenç, 2023. "The transport problem for non-additive measures," European Journal of Operational Research, Elsevier, vol. 311(2), pages 679-689.

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