title: Case-Based Reasoning for Insulin Bolus Advice.
abstract: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D).
  
  
  
  We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire.
  
  
  
  Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change.
  
  
  
  Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
authors: 1 Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, UK.
authors: Peter
authors: P
authors: Pesl
authors: 1 Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, UK.
authors: Pau
authors: P
authors: Herrero
authors: 2 Division of Medicine, Imperial College London, London, UK.
authors: Monika
authors: M
authors: Reddy
authors: 2 Division of Medicine, Imperial College London, London, UK.
authors: Nick
authors: N
authors: Oliver
authors: 2 Division of Medicine, Imperial College London, London, UK.
authors: Desmond G
authors: DG
authors: Johnston
authors: 1 Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, UK.
authors: Christofer
authors: C
authors: Toumazou
authors: 1 Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, UK.
authors: Pantelis
authors: P
authors: Georgiou
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy*; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D007328:Insulin / Q000008:administration & dosage*; D008297:Male; D008875:Middle Aged; D063731:Mobile Applications; D010865:Pilot Projects
chemical_list: D007004:Hypoglycemic Agents; D007328:Insulin
keywords: bolus calculator; case parameters; case-based-reasoning; decision support; diabetes management; insulin dosing algorithm