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    Rights statement: This is the author’s version of a work that was accepted for publication in IFAC-PapersOnLine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in IFAC-PapersOnLine, 50, 1, 2017 DOI: 10.1016/j.ifacol.2017.08.2274

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Adaptive biomedical treatment and robust control

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Adaptive biomedical treatment and robust control. / Clairon, Q.; Wilson, E.D.; Henderson, R.; Taylor, C.J.

In: IFAC-PapersOnLine, Vol. 50, No. 1, 07.2017, p. 12191-12196.

Research output: Contribution to journalJournal article

Harvard

Clairon, Q, Wilson, ED, Henderson, R & Taylor, CJ 2017, 'Adaptive biomedical treatment and robust control' IFAC-PapersOnLine, vol. 50, no. 1, pp. 12191-12196. https://doi.org/10.1016/j.ifacol.2017.08.2274

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Author

Clairon, Q. ; Wilson, E.D. ; Henderson, R. ; Taylor, C.J. / Adaptive biomedical treatment and robust control. In: IFAC-PapersOnLine. 2017 ; Vol. 50, No. 1. pp. 12191-12196.

Bibtex

@article{dcad59cfb9184ebe9e5dd640769512dc,
title = "Adaptive biomedical treatment and robust control",
abstract = "Abstract An adaptive treatment strategy is a set of rules for choosing effective medical treatments for individual patients. In the statistical literature, methods for optimal dynamic treatment (ODT) include Q-learning and A-learning methods, which are linked to machine learning in engineering and computer science. The research project behind this article aims to develop new methodology for both ODT and engineering control, through the integration of techniques and approaches that have been developed in both fields, with a particular focus on the problem of robustness. The methodological framework is based on a regret-regression approach from the statistical literature and non-minimal state-space methods from control. This article provides an introduction to some of these concepts and presents preliminary novel contributions based on the application of robust H∞ methods to ODT problems.",
keywords = "Linear control systems (TC2.2), optimal control (TC2.4), control of physiological, clinical variables (TC8.2)",
author = "Q. Clairon and E.D. Wilson and R. Henderson and C.J. Taylor",
year = "2017",
month = "7",
doi = "10.1016/j.ifacol.2017.08.2274",
language = "English",
volume = "50",
pages = "12191--12196",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "1",

}

RIS

TY - JOUR

T1 - Adaptive biomedical treatment and robust control

AU - Clairon, Q.

AU - Wilson, E.D.

AU - Henderson, R.

AU - Taylor, C.J.

PY - 2017/7

Y1 - 2017/7

N2 - Abstract An adaptive treatment strategy is a set of rules for choosing effective medical treatments for individual patients. In the statistical literature, methods for optimal dynamic treatment (ODT) include Q-learning and A-learning methods, which are linked to machine learning in engineering and computer science. The research project behind this article aims to develop new methodology for both ODT and engineering control, through the integration of techniques and approaches that have been developed in both fields, with a particular focus on the problem of robustness. The methodological framework is based on a regret-regression approach from the statistical literature and non-minimal state-space methods from control. This article provides an introduction to some of these concepts and presents preliminary novel contributions based on the application of robust H∞ methods to ODT problems.

AB - Abstract An adaptive treatment strategy is a set of rules for choosing effective medical treatments for individual patients. In the statistical literature, methods for optimal dynamic treatment (ODT) include Q-learning and A-learning methods, which are linked to machine learning in engineering and computer science. The research project behind this article aims to develop new methodology for both ODT and engineering control, through the integration of techniques and approaches that have been developed in both fields, with a particular focus on the problem of robustness. The methodological framework is based on a regret-regression approach from the statistical literature and non-minimal state-space methods from control. This article provides an introduction to some of these concepts and presents preliminary novel contributions based on the application of robust H∞ methods to ODT problems.

KW - Linear control systems (TC2.2)

KW - optimal control (TC2.4)

KW - control of physiological

KW - clinical variables (TC8.2)

U2 - 10.1016/j.ifacol.2017.08.2274

DO - 10.1016/j.ifacol.2017.08.2274

M3 - Journal article

VL - 50

SP - 12191

EP - 12196

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 1

ER -