Algorithm Produces Personalized Glucose Forecast for Patients with T2D

HealthDay News — A mechanistic model based on Gaussian process models and data assimilation can produce a personalized, nutrition-based glucose forecast for individuals with type 2 diabetes, according to a study published online in PLOS Computational Biology.

David J. Albers, PhD, from Columbia University in New York City, and colleagues used 3 forecasting machines to examine the glycemic impact of different meals: data assimilation, which uses Bayesian modeling to infuse data with human knowledge in a mechanistic model to generate real-time, personalized forecasts; model averaging of data assimilation output; and dynamic Gaussian process model regression.

The researchers found that the data assimilation machine estimated states and parameters using a modified dual unscented Kalman filter, thereby personalizing the mechanistic models. To make a personalized model selection for the individual and their measurement characteristics, model selection was used. The data assimilation forecasts were assessed against actual postprandial glucose measurements that individuals with type 2 diabetes captured and against predictions that were generated by diabetes educators based on reviewing nutritional records and glucose measurements for the same individual.

“Our algorithm, integrated into an easy-to-use app, predicts the consequences of eating a specific meal before the food is eaten, allowing individuals to make better nutritional choices during mealtime,” Albers said in a statement. “Now our task is to make the data assimilation tool powering the app even better.”

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Albers DJ, Levine M, Gluckman B, Ginsberg H, Hripcsak G, Mamykina L. Personalized glucose forecasting for type 2 diabetes using data assimilation [published online April 27, 2017]. Plos Comput Biol. doi:10.1371/journal.pcbi.1005232