1. Take it Easy with your Diabetes
OnTrack
Take it Easy with your Diabetes

2. OnTrack: Predictive Model
Monitor
Control
Live normally
Hayden
13 yrs old
Type 1 diabetes
PREDICTIVE
MODEL
Improved
HEALTH
INFORMED Decisions
Better
CONTROL
STRESS
FREE Experience
LOWER RISK of complications

3. OnTrack: An Artificial Neural Network
to predict blood glucose
Neural
Network
Trained with Medtronic data set
4 yrs data
Connections
48 581

4. What if I take 7.4 Insulin now?
OnTrack: Application
Prediction
What if I take 7.4 Insulin now?

5. OnTrack: Application
Prediction

6. OnTrack: Future Development
New DATA
streams
Glucose
Nutrition
Exercise
Insulin
Lifestyle
PREDICTIVE Model
Improved STATS & PREDICTABILITY

7. OnTrack: Neural Network Technology
Output Layer
Gives the “answer” from the model
Expressed as a predicted change in blood glucose (in mg/dL)
OUTPUT
Hidden layers
Learn to detect patterns /features in the input data
Sort out relevant information from irrelevant “noise”
Compute relationships between patterns
We used 2 hidden layers for the prototype OnTrack model
Input Layer
Encoded data used as input for the model
We used 240 inputs as follows:
72 changes in blood glucose from previous time periods (6 hrs)
72 carb consumption in previous time periods (6 hrs)
72 insulin delivered in previous time periods (6 hrs)
24 indicators for current hour of the day
INPUT

8. OnTrack: Data Challenges
CHALLENGES OBSERVED
LEARNINGS / IMPLICATIONS
Synchronised carb and insulin inputs
Across data set, carb and corresponding insulin dose usually given in same 5min time period
This effect makes it hard / impossible to separate the individual effect of carb and insulin intake
Ideally, data collection would be designed to clearly demonstrate individual effects of different variables
e.g. testing insulin intake separately from food consumption
Missing explanatory data
Exercise / activity data
Sleep pattern
Detailed nutrition data (food types, Glycaemic Index etc)
Biometrics data
Personal characteristics
Additional data feeds would be required to improve explanatory power of model
Model currently explains about 50% of blood glucose variation
With additional data, could perhaps explain 80-90%
Only one individual
Model learnt exclusively on the basis of a single person is unlikely to be accurate for others
Data collection should include a cross section of individuals with a range of different characteristics