Metaheuristics for Discovering Favourable Continuous Intravenous Insulin Rate Protocols from Historical Patient Data

  • Hongyu Wang
  • Lynne Chepulis
  • Ryan G. Paul
  • Michael MayoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)


Metaheuristic search algorithms such as particle swarm optimisation algorithm and covariance matrix adaptation evolution strategy are used to discover improved strategies for setting intravenous insulin rates of hospital in-patients with diabetes. We describe an approach combining and extending two existing methods recently reported in the literature: the Glucose Regulation for Intensive Care Patients (GRIP) method, and a favourability metric used for comparing competing strategies using historical medical records. We demonstrate with a dataset of blood glucose level/insulin infusion rate time series records from sixteen patients that new and significantly better insulin infusion strategies than GRIP can be discovered from this data.


  1. 1.
    Bonyadi, M.R., Michalewicz, Z.: Particle swarm optimization for single objective continuous space problems: a review. Evol. Comput. 25(1), 1–54 (2017)CrossRefGoogle Scholar
  2. 2.
    Braithwaite, S.S., Clark, L.P., Idrees, T., Qureshi, F., Soetan, O.T.: Hypoglycemia prevention by algorithm design during intravenous insulin infusion. Curr. Diab. Rep. 18(5), 26 (2018)CrossRefGoogle Scholar
  3. 3.
    Devi, R., Zohra, T., Howard, B.S., Braithwaite, S.S.: Target attainment through algorithm design during intravenous insulin infusion. Diab. Technol. Ther. 16(4), 208–218 (2014)CrossRefGoogle Scholar
  4. 4.
    Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Hansen, N.: The CMA evolution strategy: a tutorial. CoRR abs/1604.00772 (2016).
  6. 6.
    Steil, G.M., Deiss, D., Shih, J., Buckingham, B., Weinzimer, S., Agus, M.S.: Intensive care unit insulin delivery algorithms: why so many? How to choose? J. Diab. Sci. Technol. 3(1), 125–140 (2009)CrossRefGoogle Scholar
  7. 7.
    Vogelzang, M., Zijlstra, F., Nijsten, M.W.: Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit. BMC Med. Inform. Decis. Mak. 5(1), 38 (2005)CrossRefGoogle Scholar
  8. 8.
    Wong, A.F., Pielmeier, U., Haug, P.J., Andreassen, S., Morris, A.H.: An in silico method to identify computer-based protocols worthy of clinical study: an insulin infusion protocol use case. J. Am. Med. Inform. Assoc. 23(2), 283–288 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hongyu Wang
    • 1
  • Lynne Chepulis
    • 2
  • Ryan G. Paul
    • 2
    • 3
  • Michael Mayo
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand
  2. 2.Waikato Medical Research CenterUniversity of WaikatoHamiltonNew Zealand
  3. 3.Waikato Regional Diabetes ServiceWaikato District Health BoardHamiltonNew Zealand

Personalised recommendations