Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
Not on the map. Why?
Abstract (arXiv)
Online reinforcement learning (RL) algorithms offer great potential for personalizing treatment for participants in clinical trials. However, deploying an online, autonomous algorithm in the high-stakes healthcare setting makes quality control and data quality especially difficult to achieve. This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials. It emphasizes the responsibility of the algorithm to (1) safeguard participants and (2) preserve the scientific utility of the data for post-trial analyses. We also present a framework for pre-deployment planning and real-time monitoring to help algorithm developers and clinical researchers ensure algorithm fidelity. To illustrate our framework's practical application, we present real-world examples from the Oralytics clinical trial. Since Spring 2023, this trial successfully deployed an autonomous, online RL algorithm to personalize behavioral interventions for participants at risk for dental disease.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 26 Feb 2024.
Builds on
15 of 28 references.
References · 15
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
- 2016Concrete Problems in AI Safetycited by 1,582
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
- 2017A Tutorial on Thompson Samplingcited by 420
- 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 285
- 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 268
- 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
- 2019Confidence Intervals for Policy Evaluation in Adaptive Experimentscited by 95
- 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
- 2020Power Constrained Banditscited by 30
- 2016Assessing Time-Varying Causal Effect Moderation in Mobile Healthcited by 27
- 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
3 more
- 2022Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Carecited by 18
- 2021The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerationscited by 11
- 2022Statistical Inference After Adaptive Sampling for Longitudinal Datacited by 11
Built on by
Citing papers per year: 2024: 3.
3 papers.