A deployed online reinforcement learning algorithm in an oral health clinical trial
Author(s)
Type
Conference Paper
Abstract
Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.
Editor(s)
Walsh, T
Shah, J
Kolter, Z
Date Issued
2025-04-11
Date Acceptance
2025-02-01
Citation
Proceedings of the AAAI Conference on Artificial Intelligence, 2025, 39 (28), pp.28792-28800
ISSN
2159-5399
Publisher
Association for the Advancement of Artificial Intelligence
Start Page
28792
End Page
28800
Journal / Book Title
Proceedings of the AAAI Conference on Artificial Intelligence
Volume
39
Issue
28
Copyright Statement
© 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40452862
Source
39th AAAI Conference on Artificial Intelligence
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Science & Technology
Technology
Publication Status
Published
Start Date
2025-02-25
Finish Date
2025-03-04
Coverage Spatial
PA, Philadelphia
Date Publish Online
2025-04-11