Summary of Monitoring Fidelity Of Online Reinforcement Learning Algorithms in Clinical Trials, by Anna L. Trella et al.
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
by Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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 This paper proposes a critical requirement for deploying online reinforcement learning (RL) algorithms in clinical trials: algorithm fidelity. To ensure quality control and data quality, the algorithm must safeguard participants and preserve scientific utility of data for post-trial analyses. The authors present a framework for pre-deployment planning and real-time monitoring to help developers and researchers achieve algorithm fidelity. A practical application is demonstrated through the Oralytics clinical trial, which successfully deployed an autonomous RL algorithm to personalize behavioral interventions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online reinforcement learning algorithms can improve personalized treatment in clinical trials, but deploying them requires special care. The authors of this paper say we need a new focus on “algorithm fidelity.” This means making sure the algorithm keeps participants safe and makes good data for scientists to study later. They also offer tools to help developers and researchers plan ahead and keep an eye on things as they go. A real-life example shows how this works in practice. |
Keywords
* Artificial intelligence * Reinforcement learning
