Oralytics Reinforcement Learning Algorithm

• URL: http://arxiv.org/abs/2406.13127v2
• Date: Thu, 12 Sep 2024 19:16:10 GMT
• Title: Oralytics Reinforcement Learning Algorithm
• Authors: Anna L. Trella, Kelly W. Zhang, Stephanie M. Carpenter, David Elashoff, Zara M. Greer, Inbal Nahum-Shani, Dennis Ruenger, Vivek Shetty, Susan A. Murphy,
• Abstract summary: Dental disease is one of the most common chronic diseases in the United States. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimize the delivery of personalized intervention prompts to improve oral self-care (OSCB) The finalized RL algorithm was deployed in the Oralytics clinical trial, conducted from fall 2023 to summer 2024.
• Score: 5.54328512723076
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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