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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15076 |
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| _version_ | 1866910578782502912 |
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| author | Ghosh, Susobhan Guo, Yongyi Hung, Pei-Yao Coughlin, Lara Bonar, Erin Nahum-Shani, Inbal Walton, Maureen Murphy, Susan |
| author_facet | Ghosh, Susobhan Guo, Yongyi Hung, Pei-Yao Coughlin, Lara Bonar, Erin Nahum-Shani, Inbal Walton, Maureen Murphy, Susan |
| contents | The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25) than any other age group, with legalization in the multiple states contributing to a public perception that cannabis is less risky than in prior decades. To address this growing concern, we developed MiWaves, a reinforcement learning (RL) algorithm designed to optimize the delivery of personalized intervention prompts to reduce cannabis use among EAs. MiWaves leverages domain expertise and prior data to tailor the likelihood of delivery of intervention messages. This paper presents a comprehensive overview of the algorithm's design, including key decisions and experimental outcomes. The finalized MiWaves RL algorithm was deployed in a clinical trial from March to May 2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MiWaves Reinforcement Learning Algorithm Ghosh, Susobhan Guo, Yongyi Hung, Pei-Yao Coughlin, Lara Bonar, Erin Nahum-Shani, Inbal Walton, Maureen Murphy, Susan Machine Learning Artificial Intelligence The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25) than any other age group, with legalization in the multiple states contributing to a public perception that cannabis is less risky than in prior decades. To address this growing concern, we developed MiWaves, a reinforcement learning (RL) algorithm designed to optimize the delivery of personalized intervention prompts to reduce cannabis use among EAs. MiWaves leverages domain expertise and prior data to tailor the likelihood of delivery of intervention messages. This paper presents a comprehensive overview of the algorithm's design, including key decisions and experimental outcomes. The finalized MiWaves RL algorithm was deployed in a clinical trial from March to May 2024. |
| title | MiWaves Reinforcement Learning Algorithm |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15076 |