Paun, L. M., Schmidt, A. F., Mcginty, S. and Husmeier, D. (2022) Statistical Inference for Optimisation of Drug Delivery from Stents. In: Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022, ISBN 9781990800085 (doi: 10.11159/icsta22.138)
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Publisher's URL: https://avestia.com/ICSTA2022_Proceedings/files/paper/ICSTA_138.pdf
Abstract
The current study employs state-of-the-art optimisation methods for estimation of unknown parameters in a mathematical model of highly non-linear partial differential equations describing drug delivery from a drug-eluding stent. A classical optimisation scheme entails enormous run times due to the need to numerically solve the computationally expensive equations a large number of times to obtain the objective (black-box) function. We address this issue by employing an efficient global optimisation scheme, i.e. Bayesian optimisation (BO). This scheme aims to find the optimum of the black-box function by using an emulator of the original objective function to select the next query point (while balancing exploration and exploitation), and sequentially refining the emulator. Additionally, the proposed optimisation scheme is adapted to scenarios where there are hidden constraints in parameter space by incorporating a classifier that learns the infeasible parameter domains. We demonstrate that given a fixed number of expensive mathematical model evaluations, the proposed BO scheme outperforms state-of-the-art classical optimisation methods in terms of accuracy.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | This work has been funded by EPSRC, grant reference number EP/T017899/1 (research hub for statistical inference in complex cardiovascular and cardiomechanic systems) and EP/S030875/1 (research centre for multiscale soft-tissue mechanics). |
| Keywords: | Statistical inference, Bayesian optimisation, Gaussian processes, emulation, classification, drug delivery, stents. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Mcginty, Dr Sean and Paun, Dr Mihaela and Husmeier, Professor Dirk |
| Authors: | Paun, L. M., Schmidt, A. F., Mcginty, S., and Husmeier, D. |
| Subjects: | Q Science > QA Mathematics |
| College/School: | College of Science and Engineering > School of Engineering > Biomedical Engineering College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| ISSN: | 2562-7767 |
| ISBN: | 9781990800085 |
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