Diabetes Data Science
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Philip Bourne discusses the opportunities for data science in addressing diabetes. Data science involves using diverse digital data to ask and answer relevant questions, arriving at statistically significant conclusions not otherwise possible. It also involves sharing findings in a way that can improve lives. Diabetes is well-suited for data science approaches due to increasing data from genomics, wearables, electronic health records, and predictive modeling successes. However, data science must be done carefully with input from experts to account for confounders and ensure accurate outcomes for complex health issues like diabetes.
![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](https://image.slidesharecdn.com/ada062318-180623122245/85/Diabetes-Data-Science-10-320.jpg)
![Diabetes Platform
Research
Students
Healthcare
Patients
Insightful Care
Rapid Innovation
17[Adapted from Omar Khurshid]
Should biomedical research be Like Airbnb?
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Diabetes Data Science
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Prediction: Comorbidity Network for 6.2M Danes Over 14.9 Years Jensen et al 2014 Nat Comm 5:4022 13
- 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. 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
- 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. 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