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.
Original language | English (US) |
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Pages (from-to) | 86-90 |
Number of pages | 5 |
Journal | Journal of Diabetes Science and Technology |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2015 |
Keywords
- Hypoglycemia prediction
- Machine learning
- Type 2 diabetes
ASJC Scopus subject areas
- Internal Medicine
- Endocrinology, Diabetes and Metabolism
- Bioengineering
- Biomedical Engineering