Machine Learning and Data Analytics - the importance of explainability

1. Machine Learning, Deep
Learning, and support for a
Learning Health System
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe

2. Machine Learning and Data Analytics are
mainstream
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Publications in PubMed, 12/29/17
Machine Learning Deep Learning

3. Machine Learning is really
mainstream

4. Image Analysis is best when it is
easy to validate
Automatic
classification of
flowers in Apple
Photos

5. Image Analysis is best when it is
easy to validate
True but useless

6. Image Analysis is best when it is
easy to validate
Occasionally
results in
unexpected
insights

7. Image Analysis is best when it is
easy to validate
But is best when
trained

8. Cancer Moonshot is using MLDA

9. NCI – DOE pilots

10. MLDA Training requires horsepower

12. Human Cognitive Capacity

13. Sebastian Thrun

14. 4/3/2017

15. Barriers to MLDA
– Liability
– Comfort
– Training
– Usability
– Traceability
– Well characterized and understood
training, validation and ‘guardrails’

16. Questions
• How do provide data in a cognitively
friendly way that helps humans make
decisions?
• We need to:
– Reduce cognitive load
– Improve data access & ‘show your work’
– Enable timely decision support

17. Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe