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OnTrack Take it Easy with your Diabetes

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

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OnTrack Take it Easy with your Diabetes

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  1. OnTrack Take it Easy with your Diabetes

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

  3. OnTrack: The Opportunity Availability and effectiveness of sensor data Comfort with using mobile applications to manage lifestyle Capability of new predictive algorithms (Nuroko) Opportunity to help diabetic people lead healthier, happier lives

  4. OnTrack: An Artificial Neural Network to predict blood glucose Connections Prototyped by OnTrack team in less than 24hrs at Singapore HealthUpHackathon using Nuroko’s Neural Network Toolkit 4 yrs data Trained with Medtronic data set 48 581 Neural Network

  5. OnTrack: Putting it together New DATA streams PREDICTIVE Model Insulin Glucose Nutrition Exercise Lifestyle Continuous, personalized real-time prediction

  6. OnTrack: Application Prediction What is going to happen to my blood sugar?

  7. OnTrack: Application Prediction

  8. OnTrack: Future Ecosystem Patients Family, friends, carers Doctors Users and beneficiaries Realtime OnTrack predictive application Status updates, advice and community OnTracksolutions Personalised reports and analysis OnTrack.com Sensors and data feeds Ongoing research & development Foundational capabilties

  9. OnTrack Take it Easy with your Diabetes Team Members: Daniel DAHLMEIER Jerome NG Marine BOURGEOIS-POTEL Mike ANDERSON Sebastian KIESSLING

  10. Appendix

  11. 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 relationships / patterns in the 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

  12. OnTrack: Importance of data streams Proportion of B.G. variation explained (%) Unobserved / random noise Personal / lifestyle factors Activity / exercise Other nutrition Theoretical maximum predictive power Carbohydrate consumption Used in hackathon weekend Insulin dosage Historical blood glucose

  13. OnTrack: Next steps 2. Application prototype 1. Extended predictive research - Apply to more data streams (e.g. DMITRI) - Personalize to different individuals - Improve power of predictive model - Create innovative user experience - Fit with lifestyle / behavioral change - Build compelling proposition for patients & doctors

  14. OnTrack Take it Easy with your Diabetes Team Members: Daniel DAHLMEIER Jerome NG Marine BOURGEOIS-POTEL Mike ANDERSON Sebastian KIESSLING

  15. Contacts Team Lead: Mike ANDERSON Contact No.: +65 94255965 Email: mike@nuroko.com Data Sets Used: Medtronic 4yr CGM dataset

  16. 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 (Bolus Wizard inputs) • This effect makes it hard / impossible to separate the individual effects of carb and insulin intake Data collection needs to be designed to clearly demonstrate individual effects of different variables • e.g. sometimes 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

  17. OnTrack: Potential phased approach • Apply technique to more individual data sets • Test different sensor data sources (FitBit, etc.) • Refine predictive model design • First mobile application prototype • Build /acquire data infrastructure • Build mobile application • Build / establish sensor input streams • User trials and refinement • Marketing • Channel preparations • “Public Beta” • Launch preparation • Public Launch • Service operations

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