Summary
Objectives:
How accurate can trained clinicians predict blood glucose concentrations? Good clinical
treatment is, among other things, related to understanding the factors influencing
blood glucose level. We analyze trained clinician’s prediction accuracy in comparison
with selected computer-implemented prediction algorithms and models.
Methods:
We have in this study included diaries of 12 people with type 1 diabetes. This test
group consists of seven males and five females, ages 24 to 60, HbA1c 6.0 to 8.9 and a BMI between 20 and 28 kg/m2. Eight experienced clinicians tried to predict the blood glucose measurements based
on minimum three days of diary history. Selected prediction algorithms and models
were used for comparison. The reason we focus on type 1 diabetes is that it has the
most critical insulin requirement, so accurate prediction can be more critical than
for type 2.
Results:
An accuracy of 28.5% and an error of 26.7% were found from predictions made by the
clinicians. A physiological model and an artificial intelligence model showed higher
accuracy of 32.2% and 34.2% in comparison with the clinicians (p < 0.05). A simple
predictor algorithm based on the mean blood glucose history showed significant (p
< 0.05) lower total root mean square error compared to predictions made by the clinicians.
Conclusion:
To predict blood glucose level from diaries has shown to be profoundly difficult
even for experienced clinicians in comparison with predictions from computer algorithms
and models. This suggests that computer-based systems incorporating predicting algorithms
and models are likely to contribute positively to the day-to-day treatment of people
with diabetes.
Keywords
Forecasting - blood glucose - diabetes mellitus
