Oralytics Reinforcement Learning Algorithm
This addresses dental disease prevention for the general public by improving oral hygiene habits, but it is incremental as it applies existing RL methods to a new domain.
The paper tackles the problem of inconsistent oral self-care behaviors by developing Oralytics, a reinforcement learning algorithm that delivers personalized intervention prompts, which was deployed in a clinical trial from 2023 to 2024.
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.