Dental disease is still one of the most common chronic diseases in the United States. While dental disease is preventable through healthy oral self-care behaviors (OSCB), this basic behavior is not consistently practiced. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimizes the delivery of personalized intervention prompts to improve OSCB. In this paper, we offer a full overview of algorithm design decisions made using prior data, domain expertise, and experiments in a simulation test bed. The finalized RL algorithm was deployed in the Oralytics clinical trial, conducted from fall 2023 to summer 2024.
Cite
@article{arxiv.2406.13127,
title = {Oralytics Reinforcement Learning Algorithm},
author = {Anna L. Trella and Kelly W. Zhang and Stephanie M. Carpenter and David Elashoff and Zara M. Greer and Inbal Nahum-Shani and Dennis Ruenger and Vivek Shetty and Susan A. Murphy},
journal= {arXiv preprint arXiv:2406.13127},
year = {2024}
}