RL for Interventional Digital Health
Developing Reinforcement Learning Methods for Building Reliable Interventional Digital Health Systems

Methods for sequential, patient-centered digital health interventions, with a focus on reliable reinforcement learning and study design.
PCORI PI active
Funder
PCORI
Period
03/2026–04/2029
Principal Investigator
Zhenke Wu
Role
PI
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Overview

This PCORI-funded methods project develops reinforcement learning tools for interventional digital health systems. The work sits in PCORI’s Improving Methods for Comparative Effectiveness Research program (Cycle 1, 2025), under the programmatic priority areas Methods To Improve the Use of Artificial Intelligence (AI) and Machine Learning and Methods To Improve Study Design.

The goal is to support reliable, patient-centered sequential decisions in digital health — when to intervene, for whom, and with what intensity — while keeping the underlying study design and evaluation statistically valid.

Project period through April 2029. PI: Zhenke Wu, University of Michigan. Award Number: ME-2025C1-44006

Contact: zhenkewu[arroba]umich[punto]edu

This page is also the project’s resource-sharing site. Publications, presentations, workshop tutorials, conference talks (including AI and Healthcare and JSM), and advisory meeting materials are listed below and will be updated as they become available.

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Dissemination and resources
Publications, presentations, workshop tutorials, conference talks (including AI and Healthcare and JSM), and advisory meeting materials.
Publications 4
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Presentations 0
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None posted yet. New materials will appear here.
Workshop and tutorial materials 0
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None posted yet. New materials will appear here.
Conference presentations 0
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AI and Healthcare, JSM, and other conference presentations.
None posted yet. New materials will appear here.
Advisory meeting materials 0
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None posted yet. New materials will appear here.