A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications

Journal of the Royal Society Interface

Volumn 8, Issue 54, 2011, Pages 44-55

  Das, A ^a   Gao, Z ^b   Menon, P P ^a   Hardman, J G ^c   Bates, D G ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Model validation; Physiological simulation; Systems engineering

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; COMPUTATIONAL EFFICIENCY; ENGINEERING; FORECASTING; GLOBAL OPTIMIZATION; MONTE CARLO METHODS; OPTIMIZATION; PHYSIOLOGY; QUADRATIC PROGRAMMING; SIMULATORS; SYSTEMS ENGINEERING;

BEST-PRACTICES; CLINICAL APPLICATION; CLINICAL ENVIRONMENTS; DIRECT SEARCH; ENGINEERING FRAMEWORKS; GAS PRESSURES; GLOBAL OPTIMIZATION METHOD; IN-SILICO; IN-VIVO; MEDICAL PRACTITIONER; MODEL PARAMETERS; MODEL PREDICTION; MODEL VALIDATION; MONTE CARLO SIMULATION; OPTIMIZATION METHOD; PARAMETRIC UNCERTAINTIES; PHYSIOLOGICAL PRINCIPLES; PHYSIOLOGICAL SIMULATION; PHYSIOLOGICAL SIMULATOR; POPULATION HETEROGENEITY; PULMONARY PHYSIOLOGY; SEQUENTIAL QUADRATIC PROGRAMMING; SIMULATION MODEL; STATISTICAL APPROACH; SYSTEMS AND CONTROL; UNCERTAINTY MODELLING;

PHYSIOLOGICAL MODELS;

ARTERIAL GAS; ARTICLE; BIOMEDICAL ENGINEERING; BLOOD GAS TENSION; CARDIOPULMONARY FUNCTION; COMPUTER MODEL; GENETIC ALGORITHM; IN VIVO STUDY; MONTE CARLO METHOD; RELIABILITY; SIMULATION; SIMULATOR; VALIDATION PROCESS;

EID: 78650816133     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2010.0224     Document Type: Article

References (24)

1. Hardman, J. G. & Ross, J. J. 2006 Modelling: a core technique in anaesthesia and critical care research. Br. J. Anaesth. 97, 589-592.
2. Hardman, J., Bedforth, N., Ahmed, A., Mahajan, R. & Aitkenhead, A. 1998 A physiology simulator: validation of its respiratory components and its ability to predict the patient's response to changes in mechanical ventilation. Br. J. Anaesth. 81, 327-332.
3. Mccahon, R. A., Columb, M. O., Mahajan, R. P. & Hardman, J. G. 2008 Validation and application of a high-fidelity, computational model of acute respiratory distress syndrome to the examination of the indices of oxygenation at constant lung-state. Br. J. Anaesth. 101, 358-365. (doi:10.1093/bja/aen181)
4. Severinghaus, J. W. 1979 Simple, accurate equations for human blood O2 dissociation computations. J. Appl. Physiol. 46, 599-602.
5. Morohashi, M., Winn, A. E., Borisuk, M. T., Bolouri, H., Doyle, J. & Kitano, H. 2002 Robustness as a measure of plausibility in models of biochemical networks. J. Theor. Biol. 216, 19-30. (doi:10.1006/jtbi.2002.2537)
6. Kim, J., Bates, D. G., Postlewaite, I., Ma, L. & Iglesias, P. A. 2006 Robustness analysis of biochemical network models. IET Syst. Biol. 153, 96-104.
7. Kim, J.-S., Valeyev, N. V., Postlethwaite, I., Heslop-Harrison, P., Cho, K.-H. & Bates, D. G. 2009 Analysis and extension of a biochemical network model using robust control theory. Int. J. Robust Nonlinear Control 20, 1017-1026. (doi:10.1002/rnc.1528)
8. Fielding, C., Varga, A., Bennani, S. & Selier, M. 2002 Advanced techniques for the clearance of flight control laws. Berlin, Germany: Springer Verlag.
9. Menon, P. P., Bates, D. G. & Postlethwaite, I. 2006 Computation of worst-case pilot inputs for nonlinear flight control system analysis. J. Guid. Control Dyn. 29, 195-199. (doi:10.2514/1.14687)
10. Menon, P. P., Bates, D. G. & Postlethwaite, I. 2007 Nonlinear robustness analysis of flight control laws for highly augmented aircraft. Control Eng. Pract. 15, 655-662. (doi:10.1016/j.conengprac.2006.11.015)
11. Menon, P. P., Postlethwaite, I., Bennani, S., Marcos, A. & Bates, D. G. 2009 Robustness analysis of a reusable launch vehicle flight control law. Control Eng. Pract. 17, 751-765. (doi:10.1016/j.conengprac.2008.12.002)
12. Hardman, J. G. 2001 Respiratory physiological modelling - the design, construction, validation and application of a set of original respiratory physiological models. PhD thesis, Division of Anaesthesia and Intensive Care, University of Nottingham, UK.
13. 2: a factor analysis using a physiological simulation. Anaesth. Intensive Care 27, 452-458.
14. Hardman, J. G. & Bedforth, N. 1999 Estimating venous admixture using a physiological simulator. Br. J. Anaesth. 82, 346-349.
15. 2. J. Clin. Invest. 54, 54-68. (doi:10.1172/JCI107750)
16. Fletcher, R. & Powell, M. 1963 A rapidly convergent descent method for minimization. Comp. J. 6, 163.
17. Mitchell, M. 1998 An introduction to genetic algorithms. Cambridge, MA: MIT Press.
18. Audet, C. & Dennis Jr, J. 2007 Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17, 188-217. (doi:10.1137/ 040603371)
19. Vidyasagar, M. 1998 Statistical learning theory and randomized algorithms for control. IEEE Control Syst. Mag. 18, 69-85. (doi:10.1109/37.736014)
20. Mathworks. 2002 Optimization toolbox user's guide.
21. Mathworks. 2005 MATLAB genetic algorithm and direct search toolbox. See http://www.mathworks.com/access/helpdesk/help/toolbox/gads/.
22. Marshall, B. E., Clarke, W. R., Costarino, A. T., Chen, L., Miller, F. & Marshall, C. 1994 The dose-response relationship for hypoxic pulmonary vasoconstriction. Resp. Physiol. 96, 231-247. (doi:10.1016/0034-5687(94)90129-5)
23. Williams, P. S. 2001 A Monte Carlo Dispersion Analysis of the X-33 Simulation Software. In Proc. AIAA Conf. on Guidance, Navigation and Control, Montreal, Canada.
24. Goldberg, D. E. 1989 Genetic algorithms in search and optimization. Reading, MA: Addison-wesley.