The Challenge of Predicting Meal-to-meal Blood Glucose Concentration changes for Patients with Type I Diabetes
Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia. They must also be careful not to inject too much insulin because this could induce (potentially fatal) hypoglycemia. Patients therefore follow a “regimen” that determines how much insulin to inject at each time, based on various measurements. We can produce an effective regimen if we can accurately predict a patient’s future blood glucose (BG) values from his/her current features. This study explores the challenges of predicting future BG by applying a number of machine learning algorithms, as well as various data preprocessing variations (corresponding to 312 [learner, preprocessed-dataset] combinations), to a new T1D dataset that contains 29,601 entries from 47 different patients. Our most accurate predictor, a weighted ensemble of two Gaussian Process Regression models, achieved a (cross-validation) 𝑒𝑟𝑟𝐿1 loss of 2.7 mmol/L (48.65 mg/dl). This result was unexpectedly poor given that one can obtain an 𝑒𝑟𝑟𝐿1 of 2.9 mmol/L (52.43 mg/dl) using the naive approach of simply predicting the patient’s average BG. These results suggest that the diabetes diary data that is typically collected may be insufficient to produce accurate BG prediction models; additional data may be necessary to build accurate BG prediction models over hours.
Citation
N. Borle,
E. Ryan,
R. Greiner.
"The Challenge of Predicting Meal-to-meal Blood Glucose Concentration changes for Patients with Type I Diabetes". Health Informatics Journal, January 2021.
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BibTeX
@article{Borle+al:21,
author = {Neil Borle and Edmond Ryan and Russ Greiner},
title = {The Challenge of Predicting Meal-to-meal Blood Glucose Concentration
changes for Patients with Type I Diabetes},
journal = {Health Informatics Journal},
year = 2021,
}Last Updated: August 02, 2021
Submitted by Russ Greiner 
