MODELLING GLYCAEMIA IN ICU PATIENTS - A Dynamic Bayesian Network Approach

Catherine G. Enright, Michael G. Madden, Stuart Russell, Norm Aleks, Geoffrey Manley, John Laffey, Brian Harte, Anne Mulvey, Niall Madden

2010

Abstract

Presented in this paper is a Dynamic Bayesian Network (DBN) approach to predict glycaemia levels in intensive care patients. The occurrence of hyperglycaemia is associated with increased morbidity and mortality in critically ill patients. Due to the large inter-patient and intra-patient variability, the sparse nature of observations, inaccuracies in the data and the large number of factors that influence glycaemia, the system being modelled contains several sources of uncertainty. In the context of this uncertainty, the DBN-based system presented here performs extremely well. By using a DBN we integrate multiple strands of temporal evidence, arriving at varying time intervals, to determine the most probable underlying explanations. A key contribution of this work is that it presents a principled technique for recalibration of model parameters from general population-level values to patient-specific values, based entirely on standard real-time measurements from the patient. While in this paper we apply our approach to the glycaemia problem, this approach is equally applicable to other applications where unseen variables must be assessed and individualized in real time.

References

  1. Abkai, C. & Hesser, J., 2009. Virtual intensive care unit (ICU): real-time simulation environment applying hybrid approach using dynamic Bayesian Networks and ODEs. Studies in Health Technology and Informatics, 142, 1-6.
  2. Aleks, N., Russell, S., Madden, M.G., Morabito, D., Staudenmayer, K., Cohen, M., Manley, G., 2008. Probabilistic detection of short events, with application to critical care monitoring. Proceedings of NIPS 2008: 22nd Annual Conference on Neural Information Processing Systems, Vancouver, Canada.
  3. Andersen KE, Højbjerre M., 2003. A Bayesian approach to Bergman's minimal model. In C. M. Bishop and B. J. Frey (eds), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Jan 3-6, 2003, Key West, FL.
  4. Bergman, R.N., Phillips, L.S. & Cobelli, C., 1981. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. Journal of Clinical Investigation, 68(6), 1456-1467.
  5. Charitos, T., Van der Gaag, L.C., Visscher, S., Schurink, K.A. & Lucas, P.J., 2009. A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Systems with Applications, 36(2P1), 1249-1258.
  6. Chase J, Shaw G, Wong X, Lotz T, Lin J, Hann C., 2006. Model-based glycaemic control in critical care-A review of the state of the possible. Biomedical Signal Processing and Control, 1(1), 3-21.
  7. Van Gerven, M.A., Taal, B.G. & Lucas, P.J., 2008. Dynamic Bayesian networks as prognostic models for clinical patient management. Journal of Biomedical Informatics, 41(4), 515-529.
  8. Haverbeke N, Van Herpe T, Diehl M, Van den Berghe G, De Moor B, 2008. Nonlinear model predictive control with moving horizon state and disturbance estimation - application to the normalization of blood glucose in the critically ill. Proceedings of the 17th IFAC World Congress 2008.
  9. Hovorka, R., Chassin, L.J., Ellmerer, M., Plank, J. and Wilinska, M.E.:,2008. A simulation model of glucose regulation in the critically ill. Physiological Measurement, 29, 959-978.
  10. Iserles, Arieh, 2009. A first course in the numerical analysis of differential equations. Second edition. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge.
  11. Lin, J., Lee, D., Chase, J., Shaw,G., Le Compte, A., Lotz, T., Wong, J., Lonergan, T., Hann, C.,2008. Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care. Comput. Methods Prog. Biomed., 89(2), 141-152.
  12. Russell, S. & Norvig, P., 2002. Artificial Intelligence: A Modern Approach (2nd Edition), Prentice Hall.
  13. The NICE-SUGAR Study Investigators, Finfer S, Chittock DR, Su SY, Blair D, Foster D, Dhingra V, Bellomo R, Cook D, Dodek P, Henderson WR, Hébert PC, Heritier S, Heyland DK, McArthur C, McDonald E, Mitchell I, Myburgh JA, Norton R, Potter J, Robinson BG, Ronco JJ. et al., 2009. Intensive versus Conventional Glucose Control in Critically Ill Patients. New England Journal of Medicine, 360(13), 1283-1297.
  14. Van Herpe, T., Espinoza, M. & Haverbeke, N., De Moor, B., Van den Berghe, G.2007. Glycemia prediction in critically ill patients using an adaptive modeling approach. Journal of Diabetes Science and Technology, 1(3), 348-356.
  15. Van den Berghe, G., P. Wouters, P. ,Weekers, F., Verwaest, C., Bruyninckx, F., Schetz, M., Vlasselaers, D., Ferdinande, P., Lauwers, P., Bouillon, R., 2001. Intensive Insulin Therapy in Critically Ill Patients. N Engl J Med, 345(19), 1359-1367.
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Paper Citation


in Harvard Style

G. Enright C., G. Madden M., Russell S., Aleks N., Manley G., Laffey J., Harte B., Mulvey A. and Madden N. (2010). MODELLING GLYCAEMIA IN ICU PATIENTS - A Dynamic Bayesian Network Approach . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 452-459. DOI: 10.5220/0002750804520459


in Bibtex Style

@conference{biosignals10,
author={Catherine G. Enright and Michael G. Madden and Stuart Russell and Norm Aleks and Geoffrey Manley and John Laffey and Brian Harte and Anne Mulvey and Niall Madden},
title={MODELLING GLYCAEMIA IN ICU PATIENTS - A Dynamic Bayesian Network Approach},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={452-459},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002750804520459},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - MODELLING GLYCAEMIA IN ICU PATIENTS - A Dynamic Bayesian Network Approach
SN - 978-989-674-018-4
AU - G. Enright C.
AU - G. Madden M.
AU - Russell S.
AU - Aleks N.
AU - Manley G.
AU - Laffey J.
AU - Harte B.
AU - Mulvey A.
AU - Madden N.
PY - 2010
SP - 452
EP - 459
DO - 10.5220/0002750804520459