Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913465918029824 |
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| author | Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Yan, Iris Doshi-Velez, Finale Murphy, Susan A. |
| author_facet | Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Yan, Iris Doshi-Velez, Finale Murphy, Susan A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17003 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Yan, Iris Doshi-Velez, Finale Murphy, Susan A. Machine Learning Artificial Intelligence Computers and Society 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. |
| title | Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2402.17003 |