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Diabetes Data Science

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Presentation at the American Diabetes Association 78th Meeting, Orlando, Florida, June 23, 2018

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Diabetes Data Science

  1. 1. Diabetes Data Science Philip E. Bourne PhD, FACMI Stephenson Chair of Data Science Director, Data Science Institute Professor of Biomedical Engineering peb6a@virginia.edu https://www.slideshare.net/pebourne 1 @pebourne American Diabetes Association, June 23, 2018, Orlando
  2. 2. I declare no conflicts of interest … I am an open science advocate and you can take all the photos you want (being courtious to others) ... The slides are all on slideshare in any case 2
  3. 3. I am not a diabetes researcher... I am a computational biologist come data scientist interested in helping address diabetes where I see lots of opportunities 3
  4. 4. So What is Data Science? 4 http://vadlo.com/cartoons.php?id=357 Data science is like the Internet… If I asked you to define it, you would all say something different, yet you use it every day…
  5. 5. So What do I Mean by Data Science? • Use of the ever increasing amount of open, complex, diverse digital data • Finding ways to ask and then answer relevant questions by combining such diverse data sets • Arriving at statistically significant conclusions not otherwise obtainable • Sharing such findings in a useful way • Translating such findings into actions that improve the human condition 5
  6. 6. If you don’t listen to me listen to: The NIH Strategic Plan for Data • Support a Highly Efficient and Effective Biomedical Research Data Infrastructure • Promote Modernization of the Data-Resources Ecosystem • Support the Development and Dissemination of Advanced Data Management, Analytics, and Visualization Tools • Enhance Workforce Development for Biomedical Data Science • Enact Appropriate Policies to Promote Stewardship and Sustainability 6https://grants.nih.gov/grants/rfi/NIH-Strategic-Plan-for-Data-Science.pdf
  7. 7. Why Now? Drivers of Change • Generic • There are ~2.7 Zetabytes (2.7 x 106 PB) of digital data • Training data is doubling every two years • Robust and reusable tools in Python and R • More advanced tools e.g., Deep Artificial Neural Networks (DNNs) • New computing power e.g., GPUs, the cloud • Advances coming from the private sector NOT academia • Successful integration into workflows & lifestyles – analytics companies • Diabetes specific • $1000 genome • Wearable sensors • Mandatory EHRs • “Success” in predictive modelling 7 Pastur-Romay et al. 2016 doi:10.3390/ijms17081313
  8. 8. Mapping Diabetes to the 5 Pillars of Data Science 8 Data Integration & Engineering Machine Learning & Analytics Visualization & Dissemination Data Acquisition Ethics, Law, Policy, Social Implications
  9. 9. Mapping Diabetes to the 5 Pillars of Data Science 9 Data Integration & Engineering Machine Learning & Analytics Visualization & Dissemination Data Acquisition Ethics, Law, Policy, Social Implications
  10. 10. Global Treatment Ecosystem Virtual Image of the Patient (VIP) Patient Profile; Analytics Treatment & Control Predictive Analysis Database Add Genotype, Medical Record Local Treatment Ecosystem: Real-time data; Predictive analytics; Artificial Pancreas [Adapted from Boris Kovatchev] Screening Hypoglycemia Insulin associated weight gain Retinopathy Neuropathy Nephropathy Heart disease Cichosz et al 2016 J Diabetes Sci & Tech 10(1) 27-34 10
  11. 11. Prediction – Image Recognition • Google Diabetic Retinopathy– Prediction based of training from 120,000 images classified by 54 ophthalmologists • Prediction maps inputs (image of the retina) to outputs (a diagnosis of retinopathy) in a closed system – does not consider confounders eg if the retina had been operated on • All the required information is in the data • Researchers concluded that the algorithm’s performance was in line with board-certified ophthalmologists and retinal specialists 11Krause et al. https://doi.org/10.1016/j.ophtha.2018.01.034
  12. 12. Image Recognition - Convolutional Neural Networks Convolutional Layers Max Pooling Layers • Down sampling while maintaining key features • “Convolute” discovers the feature where ever it may reside in the image 12
  13. 13. Prediction: Comorbidity Network for 6.2M Danes Over 14.9 Years Jensen et al 2014 Nat Comm 5:4022 13
  14. 14. A Note of Caution 14 Predictive ability overemphasizes what is possible in healthcare … There are many confounders … Does enough expert knowledge (itself biased) in a complex system built into the algorithm provide accurate outcomes?
  15. 15. The Birthweight Paradox • What is the causal effect of smoking during pregnancy? • Confounders – alcohol consumption, diet, prenatal care • Need to adjust for cofounders e.g. birth weight • BUT birth weight is associated with infant mortality and maternal smoking – introduces bias • Lower birth weight babies from mothers who smoked during pregnancy leads to lower mortality 15
  16. 16. 16 http://cartertoons.com/
  17. 17. Diabetes Platform Research Students Healthcare Patients Insightful Care Rapid Innovation 17[Adapted from Omar Khurshid] Should biomedical research be Like Airbnb? doi: 10.1371/journal.pbio.2001818
  18. 18. In Summary • Data science will have an increasing impact on diabetes research • Data scientists & experts need to work together • Acceptance begins with getting clinicians on-board at the start of the study • Education in these new approaches is desperately needed • Bioethical data science training is part of that education even though policy and law are not keeping pace 18

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