Zobrazeno 1 - 10
of 2 611
pro vyhledávání: '"Murphy Susan"'
We consider reinforcement learning (RL) for a class of problems with bagged decision times. A bag contains a finite sequence of consecutive decision times. The transition dynamics are non-Markovian and non-stationary within a bag. Further, all action
Externí odkaz:
http://arxiv.org/abs/2410.14659
Autor:
Trella, Anna L., Zhang, Kelly W., Jajal, Hinal, Nahum-Shani, Inbal, Shetty, Vivek, Doshi-Velez, Finale, Murphy, Susan A.
Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended ora
Externí odkaz:
http://arxiv.org/abs/2409.02069
Autor:
Trella, Anna L., Ghosh, Susobhan, Bonar, Erin E., Coughlin, Lara, Doshi-Velez, Finale, Guo, Yongyi, Hung, Pei-Yao, Nahum-Shani, Inbal, Shetty, Vivek, Walton, Maureen, Yan, Iris, Zhang, Kelly W., Murphy, Susan A.
Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this
Externí odkaz:
http://arxiv.org/abs/2409.10526
Autor:
Ghosh, Susobhan, Guo, Yongyi, Hung, Pei-Yao, Coughlin, Lara, Bonar, Erin, Nahum-Shani, Inbal, Walton, Maureen, Murphy, Susan
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 co
Externí odkaz:
http://arxiv.org/abs/2408.15076
Adaptive treatment assignment algorithms, such as bandit and reinforcement learning algorithms, are increasingly used in digital health intervention clinical trials. Causal inference and related data analyses are critical for evaluating digital healt
Externí odkaz:
http://arxiv.org/abs/2407.15377
Autor:
Trella, Anna L., Zhang, Kelly W., Carpenter, Stephanie M., Elashoff, David, Greer, Zara M., Nahum-Shani, Inbal, Ruenger, Dennis, Shetty, Vivek, Murphy, Susan A.
Dental disease is still one of the most common chronic diseases in the United States. While dental disease is preventable through healthy oral self-care behaviors (OSCB), this basic behavior is not consistently practiced. We have developed Oralytics,
Externí odkaz:
http://arxiv.org/abs/2406.13127
Online Reinforcement Learning (RL) is typically framed as the process of minimizing cumulative regret (CR) through interactions with an unknown environment. However, real-world RL applications usually involve a sequence of tasks, and the data collect
Externí odkaz:
http://arxiv.org/abs/2403.10946
Imagine if AI decision-support tools not only complemented our ability to make accurate decisions, but also improved our skills, boosted collaboration, and elevated the joy we derive from our tasks. Despite the potential to optimize a broad spectrum
Externí odkaz:
http://arxiv.org/abs/2403.05911
Autor:
Ghosh, Susobhan, Guo, Yongyi, Hung, Pei-Yao, Coughlin, Lara, Bonar, Erin, Nahum-Shani, Inbal, Walton, Maureen, Murphy, Susan
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis us
Externí odkaz:
http://arxiv.org/abs/2402.17739
Autor:
Trella, Anna L., Zhang, Kelly W., Nahum-Shani, Inbal, Shetty, Vivek, Yan, Iris, Doshi-Velez, Finale, Murphy, Susan A.
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 da
Externí odkaz:
http://arxiv.org/abs/2402.17003