Summary of A Deployed Online Reinforcement Learning Algorithm in An Oral Health Clinical Trial, by Anna L. Trella et al.
A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
by Anna L. Trella, Kelly W. Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents Oralytics, a mobile health (mHealth) intervention system designed to improve oral self-care behaviors and prevent dental disease among marginalized individuals. The system incorporates an online reinforcement learning algorithm that determines optimal times to deliver prompts encouraging tooth brushing and other preventative measures. The authors highlight key design decisions made for the algorithm to address challenges in the clinical trials setting in the U.S. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Oralytics is a new way to help people take better care of their teeth. Many people don’t follow good oral hygiene habits, which can lead to big problems like tooth decay and even health issues elsewhere in the body. The researchers created Oralytics to make it easier for people to do what they know they should be doing – brushing their teeth regularly. This system uses a special kind of learning to figure out when to remind people to brush their teeth. It’s being tested in a clinical trial to see if it really works. |
Keywords
» Artificial intelligence » Reinforcement learning
