Take it Easy with your Diabetes

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Presentation transcript:

Take it Easy with your Diabetes OnTrack Take it Easy with your Diabetes

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

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

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

OnTrack: Application Prediction

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

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

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