Artificial Intelligence in Medicine
Volume 45, Issue 1 , Pages 53-62 , January 2009

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part II. Clinical implementation and evaluation

  • Jonathan J. Ross

      Affiliations

    • Northern General Hospital, Sheffield S5 7AU, United Kingdom
  • ,
  • Mouloud A. Denaï

      Affiliations

    • Department of Automatic Control & Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom
  • ,
  • Mahdi Mahfouf

      Affiliations

    • Department of Automatic Control & Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom
    • Corresponding Author InformationCorresponding author. Tel.: +44 114 222 5607; fax: +44 114 222 5624.

Received 1 July 2008 ,Revised 2 September 2008 ,Accepted 6 November 2008.

References 

  1. Day J, Taylor K. The systemic inflammatory response syndrome and cardiopulmonary bypass. International Journal of Surgery. 2005;3(2):129–140
  2. Kristof AS, Magder S. Low systemic vascular resistance state in patients undergoing cardiopulmonary bypass. Critical Care Medicine. 1999;27:1121–1137
  3. Gelman S. Complications during vascular surgery: basic principles and management of arterial hypotension and hypertension. Baillière's Clinical Anesthesiology. 2000;14(1):111–124
  4. Ross JJ, Mahfouf M, Denaï MA, King OK. Modeling decision-making strategies in supporting the shocked patient. British Journal of Anaesthesia. 2006;97:432P
  5. Denaï MA, Mahfouf M, Ross JJ. A hybrid hierarchical decision support system for cardiac surgical intensive care patients. In: The Anesthetic Research Society Meeting. Plymouth, United Kingdom. July 5–6, 2007. 2007;
  6. Denaï MA, Mahfouf M, Ross JJ. A fuzzy decision support system for therapy administration in cardiovascular intensive care patients. In: Proceedings of the IEEE International Conference on Fuzzy Systems. London, England, July 23–26, 2007. 2007;
  7. Devillard N. Fast median search: an ANSI C implementation, http://ndevilla.free.fr/median/ (Accessed: May 2006).
  8. Babuska R. Fuzzy modeling for control. Boston: Kluwer Academic Publisher; 1998;
  9. Mason DG, Ross JJ, McGuinness SP, Linkens DA. Multi-input, multi-output self-learning fuzzy control for hemodynamic support of septic shock. Biomedical Engineering: Applications, Basis and Communications. 1998;10:247–256
  10. Mason DG, Ross JJ, Edwards ND, Linkens DA, Reilly CS. Self-learning fuzzy control of atracurium-induced neuromuscular block during surgery. Medical and Biological Engineering and Computing. 1997;35:492–495
  11. Ross JJ, Mason DG, Linkens DA, Edwards ND. Performance assessment of a self-learning fuzzy controller of atracurium induced neuromuscular block. British Journal of Anaesthesia. 1997;78:412–415
  12. Lau F. A clinical decision-support system prototype for cardiovascular intensive care. International Journal of Clinical Monitoring and Computing. 1994;11:157–169
  13. Rao RR, Aufderheide B, Bequette BW. Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables. IEEE Transaction on Biomedical Engineering. 2000;47(11):277–288
  14. Woodruff EA, Martin JF, Omens M. A model for the design and evaluation of algorithms for closed-loop cardiovascular therapy. IEEE Transaction on Biomedical Engineering. 1997;44:694–705
  15. Barney EH, Kaufman H. Model reference adaptive control of cardiac output and blood pressure through two drug infusions. In:  Meystel A,  Herath J,  Gray S editor. Proceedings of the 5th International Symposium On Intelligent Control. Philadelphia, USA, September 5–7, 1990. 1990;p. 739–744
  16. Bauer JA, Balthasar JP, Fung HL. Application of pharmacodynamic modeling for designing time-variant dosing regimes to overcome nitroglycerine tolerance in experimental heart failure. Pharmaceutical Research. 1997;14:1140–1145

PII: S0933-3657(08)00180-2

doi: 10.1016/j.artmed.2008.11.010

Artificial Intelligence in Medicine
Volume 45, Issue 1 , Pages 53-62 , January 2009