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Performance Assessment of Estimation Methods for CIR/ISF in Bolus Calculators

  • Florian Reiterer
  • , Harald Kirchsteiger
  • , Andrea Assalone
  • , Guido Freckmann
  • , Luigi Del Re

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Type 1 diabetes patients typically determine their bolus insulin needs using a bolus calculator which requires patient- and daytime-specific values for the carbohydrate-to-insulin-ratio (CIR) as well as for the insulin-sensitivity-factor (ISF). The strong inter- and intra-patient variability of blood glucose dynamics and the myriad of influencing disturbances makes estimating these factors a difficult task that is normally executed by skilled diabetologists using high quality data. In the scientific literature different methods have been proposed that should help medical professionals in determining reliable estimates of CIR and ISF. In this paper some of these methods are first introduced and then applied on one dataset recorded in a recent clinical trial. Their performance is then evaluated by comparing the estimates of CIR and ISF with the patient- and daytime-specific values used by the patients in the clinical trial. It was found that methods based on detailed data analysis perform especially well for predicting CIR, whereas simple rules of thumb tend to give better results for predicting ISF.
Original languageEnglish
Title of host publication9th IFAC Symposium on Biological and Medical Systems (BMS 2015)
Pages231-236
Number of pages6
Volume28
Edition20
DOIs
Publication statusPublished - Sept 2015

Publication series

NameIFAC-PapersOnLine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fields of science

  • 206002 Electro-medical engineering
  • 207109 Pollutant emission
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202034 Control engineering
  • 203027 Internal combustion engines
  • 206001 Biomedical engineering

JKU Focus areas

  • Mechatronics and Information Processing

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