A Multiple Hypothesis Approach to Estimating Meal Times in Individuals With Type 1 Diabetes

Abstract

Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach.

Methods: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set (n = 11) and with either uniform or normally distributed (μ = 0, σ = 10 or 20 minutes) prior probabilities for the hypothesis set.

Results: For the simulation data set, meals were estimated with an average error of -0.77 (±7.94) minutes when uniform priors were used and -0.99 (±8.55) and -0.88 (±7.84) for normally distributed priors (σ = 10 and 20 minutes). For the clinical data set, the average estimation error was 0.02 (±30.87), 1.38 (±21.58), and 0.04 (±27.52) for the uniform priors and normal priors (σ = 10 and 20 minutes).

Conclusion: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.

Keywords: data authenticity; meal time estimation; multiple hypotheses; type 1 diabetes.

Figure 1.

Hypothesis structure for posterior probability calculation.

Figure 2.

The posterior probability of the hypothesis set as well as their respective predicted glucose values and when insulin and meals occurred. Posterior probability and predicted blood glucose value from the true hypothesis shown in black.