Patient-Specific Modeling for Critical Care

  • Maxwell Lewis Neal


Decision-making in critical care occurs on a time scale of hours, minutes, or even seconds and requires synthesizing large amounts of patient-specific (PS) data. It is therefore sensible to make use of PS modeling applications in critical care since they offer tools for integrating disparate data into a single system view and leverage computing power to provide decision support information in a timely manner. PS modeling can be used to aid diagnosis, to estimate occult physiological variables, and to test potential therapies in silico before administering them to a patient. They can therefore help clinicians determine what happened to the patient in the past, what is happening in the present, and what will happen in the future.


Traumatic Brain Injury Critical Care Total Blood Volume Semantic Interoperability Control Blood Glucose Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



My thanks to Dr. Daniel Cook who provided valuable suggestions for improving this chapter. This work was supported by American Heart Association Award 09PRE210064.


  1. 1.
    Baldwin, C.Y. and K.B. Clark, Design Rules Volume I: The Power of Modularity. 2000, Cambridge: The MIT Press. 471.Google Scholar
  2. 2.
    Bergman, R.N., Minimal model: perspective from 2005. Hormone Research, 2005. 64: p. 8–15.PubMedCrossRefGoogle Scholar
  3. 3.
    Bergman, R.N. et al., Quantitative estimation of insulin sensitivity. American Journal of Physiology- Gastrointestinal and Liver Physiology, 1979. 236(6): p. 667.Google Scholar
  4. 4.
    BOMs Homepage. Accessed 2009.
  5. 5.
    Chase, J.G. et al., Implementation and evaluation of the SPRINT protocol for tight ­glycaemic control in critically ill patients: a clinical practice change. Critical Care, 2008. 12(2): p. R49.PubMedCrossRefGoogle Scholar
  6. 6.
    Cook, D.L. et al., Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology, American Medical Informatics Association Annual Symposium Proceedings. Washington, D.C., 2008: p. 136–140.Google Scholar
  7. 7.
  8. 8.
    De Wilde, R.B.P. et al., An evaluation of cardiac output by five arterial pulse contour techniques during cardiac surgery. Anaesthesia, 2007. 62(8): p. 760.PubMedCrossRefGoogle Scholar
  9. 9.
    Degtyarenko, K. et al., ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Research, 2007. 36, Database issue: p. D344–D350.Google Scholar
  10. 10.
    Frank, O., Die grundform des arteriellen pulses. Zeitschrift Fur Biologie, 1899. 37: p. 483–526.Google Scholar
  11. 11.
    Gennari, J.H. et al., Integration of multi-scale biosimulation models via light-weight semantics. Pacific Symposium on Biocomputing, 2008. 13: p. 414–425.Google Scholar
  12. 12.
    Gennari, J.H. et al., Using Multiple Reference Ontologies: Managing Composite Annotations. Proceedings of the International Conference on Biomedical Ontology. Buffalo, NY., 2009: p. 83–86.Google Scholar
  13. 13.
    Gene Ontology. Accessed 2009.
  14. 14.
    Hann, C.E. et al., Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model. Computer Methods and Programs in Biomedicine, 2005. 77(3): p. 259–270.PubMedCrossRefGoogle Scholar
  15. 15.
    Heldt, T. et al., Computational modeling of cardiovascular response to orthostatic stress. Journal of Applied Physiology, 2002. 92(3): p. 1239.PubMedGoogle Scholar
  16. 16.
    Hucka, M. et al., The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 2003. 19(4): p. 524–531.PubMedCrossRefGoogle Scholar
  17. 17.
    J Sim Home Page. Accessed 2009.
  18. 18.
    Kass, D.A., Cardiac resynchronization therapy. Journal of Cardiovascular Electrophysiology, 2005. 16 Suppl 1: p. S35–S41.PubMedCrossRefGoogle Scholar
  19. 19.
    Kerckhoffs, R.C.P. et al., Effects of biventricular pacing and scar size in a computational model of the failing heart with left bundle branch block. Medical Image Analysis, 2009. 13(2):p. 362–369.PubMedCrossRefGoogle Scholar
  20. 20.
    Kouchoukos, N.T., L.C. Sheppard, and D.A. McDonald, Estimation of stroke volume in the dog by a pulse contour method. Circulation Research, 1970. 26(5): p. 611.PubMedCrossRefGoogle Scholar
  21. 21.
    Le Novere, N. et al., Minimum information requested in the annotation of biochemical models (MIRIAM). Nature Biotechnology, 2005. 23(12): p. 1509–1515.PubMedCrossRefGoogle Scholar
  22. 22.
    LiDCO Cardiac Sensor Systems for measuring Cardiac Output. 2009.
  23. 23.
    Lu, K. et al., A human cardiopulmonary system model applied to the analysis of the Valsalva maneuver. American Journal of Physiology - Heart and Circulatory Physiology, 2001. 281(6): p. H2661–H2679.PubMedGoogle Scholar
  24. 24.
    MIASE - Minimum Information About a Simulation Experiment. Accessed 2009.
  25. 25.
    Neal, M.L. and J.B. Bassingthwaighte, Subject-specific model estimation of cardiac output and blood volume during hemorrhage. Cardiovascular Engineering, 2007. 7(3): p. 97–120.PubMedCrossRefGoogle Scholar
  26. 26.
    Neal, M.L. et al., Advances in semantic representation for multiscale biosimulation: a case study in merging models. Pacific Symposium on Biocomputing, 2009. 14: p. 304–315.Google Scholar
  27. 27.
    Neal, M.L. and R.C. Kerckhoffs, Current progress in patient-specific modeling. Briefings in Bioinformatics, 2010. 11(1): p. 111–126.PubMedCrossRefGoogle Scholar
  28. 28.
    Parente, J. et al. Model Based Insulin Sensitivity As A Metabolic Marker For Sepsis In The ICU. 2008.Google Scholar
  29. 29.
    Pope, S.R. et al., Estimation and identification of parameters in a lumped cerebrovascular model. Mathematical BioSciences and Engineering, 2008. 6(1): p. 93–115.CrossRefGoogle Scholar
  30. 30.
    Rideout, V.C., Mathematical and computer modeling of physiological systems. NJ, USA: Prentice Hall. 1991.Google Scholar
  31. 31.
    Rosse, C. and J.L.V. Mejino, A reference ontology for bioinformatics: the foundational model of anatomy. Journal of Biomedical Informatics, 2003. 36: p. 478–500.PubMedCrossRefGoogle Scholar
  32. 32.
    Sagawa, K., R.K. Lie, and J. Schaefer, Translation of Otto Frank’s paper “Die Grundform des Arteriellen Pulses” Zeitschrift fur Biologie 37: 483–526 (1899). Journal of Molecular and Cellular Cardiology, 1990. 22(3): p. 253–277.PubMedCrossRefGoogle Scholar
  33. 33.
    Sanchez, R. and J.T. Mahoney, Modularity, flexibility, and knowledge management in product and organization design. Strategic Management Journal 1996. 17(Winter 1996): p. 63–76.Google Scholar
  34. 34.
    Schwid, H.A., Anesthesia simulators: technology and applications. The Israel Medical Association Journal. IMAJ-RAMAT GAN-, 2000. 2(12): p. 949–953.Google Scholar
  35. 35.
    Tolk, A., S.Y. Diallo, and C.D. Tunista, Applying the Levels of Conceptual Interoperability Model in Support of Integratability, Interoperability, and Composability for System-of-Systems Engineering. Journal of Systemics, Cybernetics and Informatics, 2008. 5(5): p. 65–74.Google Scholar
  36. 36.
    Van Herpe, T. et al., An adaptive modeling approach for predicting the glycemia of critically ill patients. Physiological Measurement, 2006. 27: p. 1057–1069.PubMedCrossRefGoogle Scholar
  37. 37.
    Wakeland, W. et al., Assessing the prediction potential of an in silico computer model of intracranial pressure dynamics. Critical Care Medicine, 2009. 37(3): p. 1079–1089.PubMedCrossRefGoogle Scholar
  38. 38.
    Wesseling, K.H. et al., Computation of aortic flow from pressure in humans using a nonlinear, three-element model. Journal of Applied Physiology, 1993. 74(5): p. 2566–2573.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Medical Education and Biomedical InformaticsUniversity of WashingtonSeattleUSA

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