Atul Butte NIPS 2017 ML4H
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My talk at NIPS 2017 ML4H on December 8, 2017. I had to remove some of the unpublished slides, but all the rest is here. Enjoy! -- Atul Butte
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Atul Butte NIPS 2017 ML4H
- 1. Translating a Trillion Points of Data into Diagnostics, Therapies and New Insights in Health and Disease atul.butte@ucsf.edu @atulbutte Atul Butte, MD, PhD Director, Institute for Computational Health Sciences University of California, San Francisco
- 2. Conflicts of Interest • Scientific founder and advisory board membership – Genstruct – NuMedii – Personalis – Carmenta • Honoraria for talks – Lilly – Pfizer – Siemens – Bristol Myers Squibb – AstraZeneca – Roche – Genentech – Warburg Pincus • Past or present consultancy – Lilly – Johnson and Johnson – Roche – NuMedii – Genstruct – Tercica – Ecoeos – Helix – Ansh Labs – Prevendia – Samsung – Assay Depot – Regeneron – Verinata – Pathway Diagnostics – Geisinger Health – Covance – Wilson Sonsini Goodrich & Rosati – Orrick – 10X Genomics – Medgenics – GNS Healthcare – Gerson Lehman Group – Coatue Management • Corporate Relationships – Northrop Grumman – Aptalis – Allergan – Astellas – Thomson Reuters – Intel – SAP – SV Angel – Progenity – Illumina • Speakers’ bureau – None • Companies started by students – Carmenta – Serendipity – Stimulomics – NunaHealth – Praedicat – MyTime – Flipora – Tumbl.in
- 3. @affymetrix
- 5. The Cancer Genome Atlas • 14 thousand cases • 39 types of cancers • 13 types of data: molecular, clinical, sequencing
- 6. 227 million substances x 1.3 million assays More than a billion measurements within a grid of 300 trillion cells 71 million meet Lipinski 5 1.2 million active substances
- 10. Preeclampsia: large cause of maternal and fetal death • Incidence • 5-8% of all pregnancies in the U.S. and worldwide • 4.1 million births in the U.S. in 2009 • Up to 300K cases of preeclampsia annually in the U.S. • Mortality • Responsible for 18% of all maternal deaths in the U.S. • Maternal death in 56 out of every 100,000 live births in US • Neonatal death in 71 out of every 100,000 live births in US • Cost • $20 billion in direct costs in the U.S. annually • Average hospital stay of 3.5 days Linda Liu Bruce Ling Matt Cooper
- 12. New blood markers for preeclampsia Linda Liu Bruce Ling Matt Cooper @MarchofDimes bit.ly/preeclamp
- 13. Need a diagnostic for preeclampsia Public big data available March of Dimes Center for Prematurity Research Data analyzed, diagnostic designed SPARK grant ($50k) Life Science Angels, other seed investors ($2 million) @CarmentaBio progenity.com bit.ly/carm_prog
- 16. Cancer Discovery 2013, 3:1. Psychiatric Drug Imipramine Shows Significant Activity Against Small Cell Lung Cancer Vehicle control Imipramine p53/Rb/p130 triple knockout model of SCLC Mice dosed after tumor formation Joel Dudley Nadine Jahchan Julien Sage Alejandro Sweet-Cordero Joel Neal @NuMedii
- 17. Bin Chen Wei Wei Li Ma Bin Yang Mei-Sze Chua Samuel So Gastroenterology, 2017
- 18. Need more drugs for more diseases Public big data available NIH funding Data analyzed, method designed Company launched, ARRA, StartX, Stanford license, first deal Claremont Creek, Lightspeed ($3.5 million) @NuMedii
- 19. The next big open data: clinical trials Download 100+ studies today Drug repositioning, new patient subsets, digital comparative effectiveness, more! immport.org Sanchita Bhattacharya Elizabeth Thomson
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- 25. Clinical Data Warehouse A Big UC Healthcare Data Analytics Platform Combining healthcare data from across the six University of California medical schools and systems
- 26. The next big data: clinical data
- 29. ML lessons I’ve learned over 20 years • Get the question right; solve the problems that health care professionals need solved – Solve the problems that health care professionals need solved: Don't just guess • And verify good questions and good unmet needs with more than one doc – Build a great diagnostic vs. understanding the biology • Perfectly lassoed variables may miss the big picture biology – Biologists and medical professionals really love explanations over black boxes • Watch out for input limiting models – Patients might not type in the right codes for their symptoms – They barely enter their own race/ethnicity – And docs? • Learn what IRB, HIPAA, BAA are. Learn what ICD-10 and CPT codes are. CLIA and CAP. – And learn patience. – Not all of us are cloud allergic. • Not everything needs deep learning • Having all data on everyone is super rare: genomics, images, and longitudinal EHR data? • Health care inefficiency is not about friction • Data integration and harmonization can happen if there is a business reason for it • Platforms and their companies are seemingly commoditized – Come to us with more medical knowledge and background. Convince us you care about this vertical. – Show us that we are going to learn more from you, than we are going to have to teach you.
- 30. Open challenges • Can’t teach a computer with half the game. Need the start and finish of medical “stories” – In medical care, need primary and tertiary care in the same database – Compound data might be available, but trials data is not – Means data integration and harmonization is a rate limiter • Need the right diversity in data – Otherwise might be extrapolating beyond what was learned – Need enough data, big amounts of data, true-positive cases • Need methods to handle complicated multi-modal data • What does validation mean? – Drug discovery: pre-clinical success? Or Phase 2 success? – Clinical accuracy? Or clinical utility? • Career fear uncertainty and doubt (FUD) in some circles – Tech recruitment – Too many startups?
- 31. UC Clinical Data Warehouse Team Executive Team • Atul Butte • Joe Bengfort • Michael Pfeffer • Tom Andriola • Chris Longhurst Steering Committee • Irfan Chaudhry • Mohammed Mahbouba • Lisa Dahm • David Dobbs • Kent Andersen • Ralph James • Jennifer Holland • Eugene Lee ETL Team • Albert Dugan • Tony Choe • Michael Sweeney • Timothy Satterwhite • Ayan Patel • Niranjan Wagle • Ralph James • Joseph Dalton Data Harmonization • Dana Ludwig • Daniella Meeker Data Quality • Momeena Ali • Jodie Nygaard Epic • Kevin Ames • Ben Jenkins • Steve Gesualdo Business Analyst • Ankeeta Shukla Hardware • Sandeep Chandra • Jeff Love • Scott Bailey • Kwong Law • Pallav Saxena Support • Jack Stobo • Michael Blum • Sam Hawgood
- 32. Support Admin and Tech Staff • Mary Lyall • Mounira Kenaani • Kevin Kaier • Boris Oskotsky • Mae Moredo • Ada Chen • University of California, San Francisco • Pricilla Chan and Mark Zuckerberg • NIH: NIAID, NLM, NIGMS, NCI, NHLBI, OD; NIDDK, NHGRI, NIA, NCATS • March of Dimes • Juvenile Diabetes Research Foundation • Hewlett Packard • Howard Hughes Medical Institute • California Institute for Regenerative Medicine • Luke Evnin and Deann Wright (Scleroderma Research Foundation) • Clayville Research Fund • PhRMA Foundation • Stanford Cancer Center, Bio-X, SPARK • Tarangini Deshpande • Kimayani Butte • Sam Hawgood and Keith Yamamoto • Isaac Kohane