Artificial Intelligence in Medicine
Volume 45, Issue 1 , Pages 35-52 , January 2009

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design

  • 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.
  • ,
  • Jonathan J. Ross

      Affiliations

    • Northern General Hospital, Sheffield S5 7AU, United Kingdom

Received 29 December 2007 ,Revised 2 September 2008 ,Accepted 6 November 2008.

References 

  1. Currey J, Botti M. The hemodynamic status of cardiac surgical patients in the initial 2-h recovery period. European Journal of Cardiovascular Nursing. 2005;4:207–214
  2. Foot CL, Frazer JF, Mullany DV. Common complications after cardiac surgery in the adult. Current Anesthesia & Critical Care. 2005;16(6):331–345
  3. Kaplan B. Evaluating informatics applications: clinical decision support systems review. International Journal of Medical Informatics. 2001;64:15–37
  4. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. British Medical Journal (BMJ). 2005;330(7494):765
  5. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Sam J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Journal of the American Medical Association. 2005;293:1223–1238
  6. Coiera E. Guide to health informatics. 2nd ed.. London: Arnold; 2003;
  7. Miller R, Geissbuhler A. Clinical diagnostic decision support systems: An overview in Clinical Decision Support Systems: Theory and Practice. New York: Springer; 2007;
  8. Schurink D, Lucas PJF, Hoepelman IM, Bonten MJM. Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units. The Lancet Infectious Diseases. 2005;5(5):305–312
  9. Frize M, Ennett CM, Stevenson M, Trigg HCE. Clinical decision support systems for intensive care units: using artificial neural networks. Medical Engineering and Physics. 2001;23(3):217–225
  10. John RI, Innocent PR. Modeling uncertainty in clinical diagnosis using fuzzy logic. IEEE Transactions on Systems, Man and Cybernetics, Part B. 2005;35(6):1340–1350
  11. Bates JHT, Young MP. Applying fuzzy logic to medical decision making in the intensive care unit. American Journal of Respiratory and Critical Care Medicine. 2003;167:948–952
  12. Licata G. Probabilistic and fuzzy logic in clinical diagnosis. Internal and Emergency Medicine. 2007;2:100–106
  13. Kwok HF, Linkens DA, Mahfouf M, Mills GH. SIVA: A hybrid knowledge-and-model-based advisory system for intensive care ventilators. IEEE Transactions on Information Technology in Biomedicine. 2004;8(2):161–172
  14. Berlin A, Sorani M, Sim I. A taxonomic description of computer-based clinical decision support systems. Journal of Biomedical Informatics. 2006;39(6):656–667
  15. Denaï M, Mahfouf M, Ross JJ. A physiological model describing dobutamine interaction with septic patients: a simulation study. In:  Kneppo P,  Hozman J editor. Proceedings of the 3rd European medical and biological engineering conference. Prague, Czech Republic, 20–25 November. 2005;
  16. Ross JJ, Mahfouf M, Denaï M, King OK. Modelling decision-making strategies in supporting the shocked patient. British Journal of Anesthesia. 2006;97:432
  17. Greenway CV. Mechanisms and quantitative assessment of drug effects on cardiac output with a new model of the circulation. Pharmacological Reviews. 1982;33(4):213–251
  18. Lobato EB, Gravenstein N, Martin TD. Milrinone, not epinephrine, improves left ventricle compliance after cardiopulmonary bypass. Journal of Cardiothoracic and Vascular Anesthesia. 2000;14(4):374–377
  19. Goldberg DE. Genetic Algorithms in Search, Optimisation and Machine Learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co.; 1989;
  20. Day J, Taylor K. The systemic inflammatory response syndrome and cardiopulmonary bypass. International Journal of Surgery. 2005;3(2):129–140
  21. Yamada T, Takeda J, Katori N, Tsuzaki K, Ochiai R. Hemodynamic effects of Milrinone during weaning from Cardiopulmonary Bypass: a comparison of patients with low and high prebypass cardiac index. Journal of Cardiothoracic and Vascular Anesthesia. 2000;14(4):367–373
  22. George M, Lehot JJ, Estanove S. Hemodynamic and biological effects of intravenous milrinone in patients with a low cardiac output syndrome following cardiac surgery. European Journal of Anesthesialogy Supplement. 1992;5:35–41
  23. Masuzawa T, Fukui Y, Smith NT. Cardiovascular simulation using a multiple modeling method on a digital computer: simulation of interaction between the cardiovascular system and angiotensin II. Journal of Clinical Monitoring. 1992;8:50–58
  24. Stergiopulos N, Meister JJ, Westerhof N. Determinants of stroke volume and systolic and diastolic aortic pressure. The American Physiology Society. 1996;H2050–H2059
  25. Ursino M. Interaction between carotid baroregulation and the pulsating heart: a mathematical model. American Journal of Physiology - Heart and Circulatory Physiology. 1998;275:H1733–H1747
  26. Denaï M, Mahfouf M, King OK, Ross JJ. Physiological modeling and analysis of the pulmonary microcirculation in septic patients. In: Feng DD, Dubois O, Zaytoon J, Carson E, editors. 6th IFAC symposium on modeling and control in biomedical systems, Reims, France, September 20–22. Elsevier IFAC Publications; 2006.

PII: S0933-3657(08)00178-4

doi: 10.1016/j.artmed.2008.11.009

Artificial Intelligence in Medicine
Volume 45, Issue 1 , Pages 35-52 , January 2009