Insulin Dysregulation Detection
Our Deep Learning classification algorithm analyzes a Person with Diabetes’ blood glucose levels to evaluate their individual blood glucose stability. We explain below the results achieved through our analysis of a publicly available dataset of People with Diabetes.
Sensitivity (Recall) of Insulin Dysregulation Classification
We illustrate below the sensitivity (true positive rate) of the algorithm’s evaluation capability. The table below illustrates our result.
Dataset
OhioT1DM
Number of Patients
12
Sensitivity
(%)
91.5
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.