Forecasting
Nocturnal Hypoglycemia
Our new analytical algorithm uses Deep Learning to forecast the occurrence of nocturnal hypoglycemia in People with Diabetes. Using Neural Network models, we anticipate Nocturnal Hypoglycemia Events (NHEs) with unprecedented accuracy of 97.8%. We explain below the results achieved through our analysis of a publicly available dataset of People with Diabetes.
Accuracy, Sensitivity and Specificity of NHE Classification
We verified the model’s performance using sensitivity (true positive rate), specificity (true negative rate) and accuracy metrics. The table below catalogs our results.
Dataset
OhioT1DM
Number of Patients
12
Sensitivity
(%)
99.2
Specificity
(%)
97.4
Accuracy
(%)
97.8
Dataset
The OhioT1DM Dataset was developed for blood glucose level prediction research. The dataset consists of 8 weeks of continuous glucose monitoring via Medtronic sensors and self-reported life-event data for 12 people with Type 1 Diabetes. The patient demographic was five females aged 20-60, and seven males aged 20-80.
Our Vision
This breakthrough has been achieved through Bio Conscious Technologies' innovative use of ML algorithms, which accurately anticipate high-risk diabetic events based on CGM data. We believe this breakthrough represents a significant advancement of Diabetes technology and has the potential to revolutionize the way medical professionals monitor and treat People with Diabetes. Our vision is to achieve modularity through ML model chaining to provide multiple decision support solutions.