Hypoglycemia Prediction Using Machine Learning Models for Patients With Type 2 Diabetes
Open Access
- 14 October 2014
- journal article
- research article
- Published by SAGE Publications in Journal of Diabetes Science and Technology
- Vol. 9 (1), 86-90
- https://doi.org/10.1177/1932296814554260
Abstract
Background: Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. Method: We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration. Results: The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Conclusions: Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models—which have been validated retrospectively and if implemented in real time—could be useful tools for reducing hypoglycemia in vulnerable patients.Keywords
This publication has 5 references indexed in Scilit:
- Hypo- and Hyperglycemia in Relation to the Mean, Standard Deviation, Coefficient of Variation, and Nature of the Glucose DistributionDiabetes Technology & Therapeutics, 2012
- Cluster-Randomized Trial of a Mobile Phone Personalized Behavioral Intervention for Blood Glucose ControlDiabetes Care, 2011
- The Contribution of Glucose Variability to Asymptomatic Hypoglycemia in Persons with Type 2 DiabetesDiabetes Technology & Therapeutics, 2011
- Statistical Hypoglycemia PredictionJournal of Diabetes Science and Technology, 2008
- Evaluation of a New Measure of Blood Glucose Variability in DiabetesDiabetes Care, 2006