Summary of Oralytics Reinforcement Learning Algorithm, by Anna L. Trella et al.
Oralytics Reinforcement Learning Algorithm
by Anna L. Trella, Kelly W. Zhang, Stephanie M. Carpenter, David Elashoff, Zara M. Greer, Inbal Nahum-Shani, Dennis Ruenger, Vivek Shetty, Susan A. Murphy
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can learn about an innovative approach to improving oral self-care behaviors (OSCB) for dental disease prevention. Researchers developed Oralytics, a reinforcement learning (RL) algorithm that delivers personalized intervention prompts online. This paper presents the design decisions behind the algorithm, including prior data analysis and experimentation in a simulation test bed. The finalized RL algorithm was deployed in a clinical trial from fall 2023 to summer 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dental disease is a big problem in the US. People know they should take care of their teeth, but many don’t do it consistently. Researchers created an online tool called Oralytics that helps people remember to take better care of their teeth. This paper explains how the tool was designed and tested. It’s a new way to help people prevent dental disease. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
