Advertisement

Case-Based Decision Support for Patients with Type 1 Diabetes on Insulin Pump Therapy

  • Cindy Marling
  • Jay Shubrook
  • Frank Schwartz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5239)

Abstract

This paper presents a case-based approach to decision support for diabetes management in patients with Type 1 diabetes on insulin pump therapy. To avoid serious disease complications, including heart attack, blindness and stroke, these patients must continuously monitor their blood glucose levels and keep them as close to normal as possible. Achieving and maintaining good blood glucose control is a difficult task for these patients and their health care providers. A prototypical case-based decision support system was built to assist with this task. A clinical research study, involving 20 patients, yielded 50 cases of actual problems in blood glucose control, with their associated therapeutic adjustments and clinical outcomes, for the prototype’s case base. The prototype operates by: (1) detecting problems in blood glucose control in large quantities of patient blood glucose and life event data; (2) finding similar past problems in the case base; and (3) offering the associated therapeutic adjustments stored in the case base to the physician as decision support. Results from structured evaluation sessions and a patient feedback survey encourage continued research and work towards a practical tool for diabetes management.

Keywords

Blood Glucose Level Diabetes Management Blood Glucose Control Match Case Nocturnal Hypoglycemia 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine 329, 977–986 (1993)Google Scholar
  2. 2.
    Bellazzi, R., Larizza, C., Montani, S., Riva, A., Stefanelli, M., d’Annunzio, G., Lorini, R., Gómez, E.J., Hernando, E., Brugués, E., Cermeno, J., Corcoy, R., de Leiva, A., Cobelli, C., Nucci, G., Prato, S.D., Maran, A., Kilkki, E., Tuominen, J.: A telemedicine support for diabetes management: The T-IDDM project. Computer Methods and Programs in Biomedicine 69, 147–161 (2002)CrossRefGoogle Scholar
  3. 3.
    Grant, R.W., Buse, J.B., Meigs, J.B.: Quality of diabetes care in U.S. academic medical centers: Low rates of medical regimen change. Diabetes Care 28, 337–442 (2005)CrossRefGoogle Scholar
  4. 4.
    Shah, B.R., Hux, J.E., Laupacis, A., Zinman, B., van Walraven, C.: Clinical inertia in response to inadequate glycemic control: Do specialists differ from primary care physicians? Diabetes Care 28, 600–606 (2005)CrossRefGoogle Scholar
  5. 5.
    American Diabetes Association: American Diabetes Association Complete Guide to Diabetes. 4 edn. Bantam, New York (2006)Google Scholar
  6. 6.
    Bichindaritz, I., Marling, C.: Case-based reasoning in the health sciences: What’s next? Artificial Intelligence in Medicine 36, 127–135 (2006)CrossRefGoogle Scholar
  7. 7.
    López de Màntaras, R., Arcos, J.L.: AI and music from composition to expressive performance. AI Magazine 23, 43–58 (2002)Google Scholar
  8. 8.
    Marling, C.R., Petot, G.J., Sterling, L.S.: Integrating case-based and rule-based reasoning to meet multiple design constraints. Computational Intelligence 15(3), 308–332 (1999)CrossRefGoogle Scholar
  9. 9.
    Bridge, D., Göker, M., McGinty, L., Smyth, B.: Case-based recommender systems. The Knowledge Engineering Review 20, 315–320 (2005)CrossRefGoogle Scholar
  10. 10.
    Bichindaritz, I.: MNAOMIA: Improving case-based reasoning for an application in psychiatry. In: Artificial Intelligence in Medicine: Applications of Current Technologies, Stanford, CA. Working Notes of the AAAI 1996 Spring Symposium (1996)Google Scholar
  11. 11.
    Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-based reasoning in CAREPARTNER: Gathering evidence for evidence-based medical practice. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 334–345. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Montani, S., Portinale, L., Leonardi, G., Bellazzi, R.: Applying case-based retrieval to hemodialysis treatment. In: McGinty, L. (ed.) Workshop Proceedings of the Fifth International Conference on Case-Based Reasoning, Trondheim, Norway (2003)Google Scholar
  13. 13.
    Marling, C., Whitehouse, P.: Case-based reasoning in the care of Alzheimer’s disease patients. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 702–715. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Marling, C., Shubrook, J., Schwartz, F.: Towards case-based reasoning for diabetes management. In: Wilson, D.C., Khemani, D. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 305–314. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Schwartz, F.L., Shubrook, J.H., Marling, C.R.: Use of case-based reasoning to enhance intensive management of patients on insulin pump therapy. Journal of Diabetes Science and Technology (in press, 2008)Google Scholar
  16. 16.
    Montani, S., Bellazzi, R.: Supporting decisions in medical applications: The knowledge management perspective. International Journal of Medical Informatics 68, 79–90 (2002)CrossRefGoogle Scholar
  17. 17.
    Montani, S., Magni, P., Bellazzi, R., Larizza, C., Roudsari, A.V., Carson, E.R.: Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients. Artificial Intelligence in Medicine 29, 131–151 (2003)CrossRefGoogle Scholar
  18. 18.
    Popow, C., Horn, W., Rami, B., Schober, E.: VIE-DIAB: A support program for telemedical glycaemic control. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds.) AIME 2003. LNCS (LNAI), vol. 2780, pp. 350–354. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Gómez, E.J., Hernando, M.E., García, A., del Pozo, F., Cermeno, J., Corcoy, R., Brugués, E., de Leiva, A.: Telemedicine as a tool for intensive management of diabetes: the DIABTel experience. Computer Methods and Programs in Biomedicine 69, 163–177 (2002)CrossRefGoogle Scholar
  20. 20.
    Hernando, M.E., Gómez, E.J., Gili, A., Gómez, M., García, G., del Pozo, F.: New trends in diabetes management: Mobile telemedicine closed-loop system. In: Duplaga, M., Zielinski, K., Ingram, D. (eds.) Transformation of Healthcare with Information Technologies. IOS Press, Amsterdam (2004)Google Scholar
  21. 21.
    Biermann, E., Dietrich, W., Rihl, J., Standl, E.: Are there time and cost savings by using telemanagement for patients on intensified insulin therapy? A randomised, controlled trial. Computer Methods and Programs in Biomedicine 69, 137–146 (2002)CrossRefGoogle Scholar
  22. 22.
    Lehmann, E.D.: AIDA (2008) (accessed February, 2008), http://www.2aida.net/welcome/
  23. 23.
    Lehmann, E.D.: Research use of the AIDA www.2aida.org diabetes software simulation program: A review. Part 1. Decision support testing and neural network training. Diabetes Technology & Therapeutics 5, 425–438 (2003)CrossRefGoogle Scholar
  24. 24.
    Boutayeb, A., Chetouani, A.: A critical review of mathematical models and data used in diabetology. Biomedical Engineering Online 5 (2006)Google Scholar
  25. 25.
    Schmidt, R., Montani, S., Bellazzi, R., Portinale, L., Gierl, L.: Case-based reasoning for medical knowledge-based systems. International Journal of Medical Informatics 64, 355–367 (2001)CrossRefGoogle Scholar
  26. 26.
    Nilsson, M., Sollenborn, M.: Advancements and trends in medical case-based reasoning: An overview of systems and system development. In: Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference – Special Track on Case-Based Reasoning, pp. 178–183. AAAI Press, Menlo Park (2004)Google Scholar
  27. 27.
    Holt, A., Bichindaritz, I., Schmidt, R., Perner, P.: Medical applications in case-based reasoning. The Knowledge Engineering Review 20, 289–292 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cindy Marling
    • 1
  • Jay Shubrook
    • 2
  • Frank Schwartz
    • 2
  1. 1.School of Electrical Engineering and Computer Science Russ College of Engineering and TechnologyOhio UniversityAthensUSA
  2. 2.Appalachian Rural Health Institute, Diabetes and Endocrine Center College of Osteopathic MedicineOhio UniversityAthensUSA

Personalised recommendations