Abstract: In this talk, Susan will discuss first solutions to some of the challenges we face in developing online RL algorithms for use in digital health interventions targeting patients struggling with health problems such as substance misuse, hypertension, and bone marrow transplantation. Digital health raises a number of challenges to the RL community including different sets of actions, each set intended to impact patients over a different time scale; the need to learn both within an implementation and between implementations of the RL algorithm; noisy environments; and a lack of mechanistic models. In all of these settings, the online line algorithm must be stable and autonomous. Despite these challenges, RL, with careful initialization, with careful management of bias/variance tradeoff, and by close collaboration with health scientists, can be successful. We can make an impact!
Dr. Susan Murphy has presented this talk on multiple occasions:
- September 13, 2024 at the e-HAIL Symposium at the University of Michigan (https://e-hail.umich.edu/symposium-2024/)
- October 10, 2024 at the MindTech Summit “Innovating Digital Behavioral Health” in Rotterdam, The Netherlands (https://www.eur.nl/en/essb/events/mindtech-summit-2024-10-10)
- November 11, 2024 at the Department of Statistics at University of Chicago (https://d3qi0qp55mx5f5.cloudfront.net/stat/docs/Statistics_Colloquium/2024-2025/11_11_2024_Murphy_Susan.pdf?mtime=1729700413)
- November 19, 2024 as part of the L. Brown Distinguished Lecture series and University of Pennsylvania (https://larry-brown.com/index.html)
- In March 2025 at the DAGSTAT in Berlin, Germany (https://dagstat2025.de/#invited-speakers )
- May 9, 2025 at the George Washington University Department of Statistics 90th Anniversary Celebration (https://statistics.columbian.gwu.edu/ai-conference-schedule)
- October 25, 2025 at Grand Rounds for the Department of Psychiatry and Behavioral Health at the Ohio State University (https://ccme.osu.edu/continuing-medical-education/grand-rounds/51475/online-reinforcement-learning-in-digital-health-interventions/10/15/2025)
