Machine Learning in Health Care
Organized by David M. Cutler, Sendhil Mullainathan, and Ziad Obermeyer
Supported by the National Institute on Aging
May 10, 2019
NBER,2nd Floor Conference Room, 1050 Massachusetts Avenue, Cambridge, MA

Friday, May 10

8:15 am

Shuttle van leaves Royal Sonesta Hotel for NBER

8:30 am

Continental Breakfast

9:00 am

Why Should Get Blood? Personalizing Medicine with Heterogeneous Treatment Effects

Jason Abaluck, Yale University and NBER
Leila Agha, Harvard University and NBER
David C. Chan Jr, University of California, Berkeley and NBER

10:00 am

Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy: A Machine Learning Experiment to Estimate Treatment Effects from Randomized Trial Data (slides)

Tony Duan, Stanford University
Pranav Rajpurkar, Harvard University
Dillon Laird, Stanford University
Andrew Ng, Stanford University
Sanjay Basu, Stanford University

11:00 am

Break

11:30 am

Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?

Michael A. Ribers, University of Copenhagen
Hannes Ullrich, DIW Berlin

12:30 pm

Using Machine Learning to Explain Socioeconomic and Racial Gaps in Pain (slides)

Emma J. Pierson, Cornell University
David M. Cutler, Harvard University and NBER
Jure Leskovec, Stanford University
Sendhil Mullainathan, Massachusetts Institute of Technology and NBER
Ziad Obermeyer, University of California, Berkeley and NBER

1:30 pm

Lunch and Panel

3:00 pm

Using Search Queries to Understand Health Information Needs in Africa

Rediet Abebe, Cornell University
Shawndra Hill, Columbia University
Jennifer Wortman Vaughan, Microsoft Research
Peter M. Small, Rockefeller Foundation
H. Andrew Schwartz, Stony Brook University

4:00 pm

Childhood Obesity Prediction and Risk Factor Analysis from Nationwide Health Records

Hagai Rossman, Weizmann Institute of Science
Smadar Shilo, Weizmann Institute of Science

5:00 pm

Adjourn

FORMAT: 45 mins presenter, 15 mins questions