Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching
In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implemen...
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| Veröffentlicht in: | Proceedings of the 2010 American Control Conference 2010-01, Vol.2010, p.5155-5160 |
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| container_title | Proceedings of the 2010 American Control Conference |
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| creator | Rutao Luo Piovoso, Michael J Zurakowski, Ryan |
| description | In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples. |
| doi_str_mv | 10.1109/ACC.2010.5530483 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Aerospace control Control systems Human immunodeficiency virus Mechanical factors Open loop systems Robust control Robust stability Robustness State estimation Supervisory control |
| title | Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching |
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