Personalized Choice and Beam Planning Need Separate Verification

Authors

  • Andrew Anderson Author
  • Dylan Mitchell Author
  • Oliver Cox Author

Abstract

Personalized treatment selection and surrogate-guided beam search both organize expensive evidence, but their mechanisms differ. A common accounting framework can include context coverage and final checking without pooling their outcome scales. The selected treatment rule and the selected numerical plan each need an independent evaluation appropriate to the action being defended. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

References

J. Du, S. Gao, and C.-H. Chen, “A Contextual Ranking and Selection Method for Personalized Medicine,” Manufacturing & Service Operations Management, vol. 26, no. 1, pp. 167–181, 2024.

H. H. Zhang, S. Gao, W. Chen, L. Shi, W. D. D’Souza, and R. R. Meyer, “A Surrogate-Based Metaheuristic Global Search Method for Beam Angle Selection in Radiation Treatment Planning,” Physics in Medicine and Biology, vol. 58, pp. 1933–1946, 2013.

Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.

Campi, Marco C., and Simone Garatti. “The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs.” SIAM Journal on Optimization, vol. 19, no. 3, 2008, pp. 1211–1230. https://doi.org/10.1137/07069821X.

Glasserman, Paul. Monte Carlo Methods in Financial Engineering. Springer, 2004. https://doi.org/10.1007/978-0-387-21617-1.

Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.

Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.

Chick, Stephen E., and Koichiro Inoue. “New Two-Stage and Sequential Procedures for Selecting the Best Simulated System.” Operations Research, vol. 49, no. 5, 2001, pp. 732–743. https://doi.org/10.1287/opre.49.5.732.10615.

Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.

Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.

Downloads

Published

2025-03-17

Issue

Section

Articles