4. Stanford AI in Medicine Imaging Center

8. Departmental Goals
Become the leading program in data science applied to biomedical data
Transform patient care in the UCLA Health System by leveraging advances in AI and Machine Learning

9. Our plan Expand research scope and educational programs
Develop AI in UCLA Programs
Leverage UCLA’s leadership in Data Sciences: Engineering School Affiliation

10. Expanded Scope New departmental faculty in Computational Genomics:
Eleazar Eskin Jason Ernst Bogdan Pasaniuc Wei Wang
Eran Halperin Jessica Li Noah Zaitlen Sriram Sankararaman
Jointly appointed with:
Statistics Anesthesiology Biological Chemistry Computer Science
Human Genetics Neurology Pathology
Growth Areas of Data Science in Biomedicine
Clinical Machine Learning
Computer Vision in Medicine
Leverage existing strength in Mathematical Modeling

11. What is AI in Medicine? Impact care: Reduce costs.
Identify high risk cases.
Improve speed and accuracy of diagnosis.
Predict treatment outcomes and side effects.
Reduce costs.
Requires research in AI and research in Medicine

12. What is NOT AI in Medicine?
Analysis of small numbers of patients and/or small numbers of features
Utilizing “Machine Learning Software”
Hiring “Data Scientists”
Information Technology

13. In Partnership with UCLA Health
Electronic Health Records (2M+ records)
Genomic Data (25,000 DNA genotypes week)
Waveforms (30,000 patients per year)
Images (1,500 imaging studies per day)
more…

14. AI in Medicine @ UCLA Led by Eran Halperin
CompMed/Anesthesiology Collaboration: Preoperative predictions of in-hospital mortality using electronic medical record data
Effort led by Eran Halperin/Ira Hofer.
Used EHR data from ~50,000 patients going through surgery.
Applied machine learning techniques to predict preoperative risk for mortality (AUC = 0.92).
Saves the time of ASA Physical Status Classification determined by the anesthesiologsits (AUC = 0.86)
Next step: Improve care and reduce costs by saving anesthesiologists time.
Creating a network of collaborations CompMed + Clinical Departments (OBGYN, Neurology, Anesthesiology)

15. AI in Medicine @ UCLA CROWDED AREA
Prediction
Insight
Discovery
Translation
CROWDED
AREA
ENABLE CLINICAL SCIENCE WITH HEALTH DATA
PARTNERSHIP WITH CLINICAL CHAIRS

16. Computational Medicine Programs
Blockchain and Secure Computation in Medicine: Leverage transformative technology breakthroughs in cryptography
Effort led by Sriram Sankararaman, Amit Sahai
Develop technology for analysis of genomic and medical data while preserving privacy and data sharing policy.
Builds upon foundational UCLA: Secure Computation (Sahai), Differential Privacy in Genetics (Sankararaman), Private Genome Analysis (Sahai, Eskin)
Computational Genomics in the Health System: Computational methods for combining genomic and health system data
Effort led by Jessica Li, Jason Ernst, Bogdan Pasaniuc
Builds upon foundational UCLA in Computational Genomics
Testbeds are UCLA Health System through AtLAs project and Depression Grand Challenge
Tech Transfer to Industry: Commercialization and Software Licensing

17. UCLA Computational Medicine is a Hub in the Scientific Community
Industry Partners
UCLA Computational Medicine (UCLA Health Data)
Academic Medical Centers
Scientific Community Leadership

18. Industry Partners
Industry Partners will translate discoveries beyond UCLA Health System
Current Partnerships
Amazon
We are looking for industry partners to:
Develop AI in Medicine Technology
Answer Medical Questions using UCLA Health Data (e.g., stratifying patients receiving drug to reduce side effects)

19. Academic Medical Center Partnerships
Goal: Establish Formal Collaborative Relationship with Health Systems which have Genomic Patient Data
Replication across Health Systems
Meta-Analysis (e.g. NIDA Opiod Consortium)
Share expertise and collaborate
Current Partnerships in Progress
Vanderbilt (Nancy Cox)
University of Michigan (Michael Bohenkhe)
Mt. Sinai (Eimear Kenney)

20. Scientific Community Leadership
Computational Genomics Summer Institute
50 Faculty and 150 participants spent 1 week to 1 month at UCLA in 2018
External Affiliate Member Program
Incorporate Scientific Community Expertise in Analyzing UCLA Health Data
Within UC Expertise
Outside UC Expertise

21. UCLA’s Potential
Best group of faculty in Computational Genomics + Machine Learning in the Country
Strong Computer Science / Engineering
International leaders in Machine Learning, Data Mining, Computer Vision and related areas.
Strong Medical School
Excellent and large Health System -> unique data.
Single Campus
A history of interdisciplinary activities.

22. UCLA Computational Medicine
New DGSOM Department (formerly Biomathematics)
Department Goals:
Become the leading program in data science applied to biomedical data
Transform patient care in the UCLA Health System by leveraging advances in AI and Machine Learning
Department Scope:
Computational Genomics
Machine Learning on Clinical Data
8 Faculty joined Biomathematics in 2018
Ongoing FTE Faculty Search
Provides bridge to Data Science Expertise on the Main Campus
Computer Vision on Medical Images
Mathematical Modeling