Zobrazeno 1 - 10
of 5 506
pro vyhledávání: '"Kelly, W"'
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
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
Real-world decision-making requires grappling with a perpetual lack of data as environments change; intelligent agents must comprehend uncertainty and actively gather information to resolve it. We propose a new framework for learning bandit algorithm
Externí odkaz:
http://arxiv.org/abs/2405.19466
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
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
Autor:
Carolyn A. Cohen, Nancy H. L. Leung, Prathanporn Kaewpreedee, Kelly W. K. Lee, Janice Zhirong Jia, Alan W. L. Cheung, Samuel M. S. Cheng, Masashi Mori, Dennis K. M. Ip, Leo L. M. Poon, J. S. Malik Peiris, Benjamin J. Cowling, Sophie A. Valkenburg
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Whole virion inactivated vaccine CoronaVac (C) and Spike (S) mRNA BNT162b2 (B) vaccines differ greatly in their ability to elicit neutralizing antibodies but have somewhat comparable effectiveness in protecting from severe COVID-19. We condu
Externí odkaz:
https://doaj.org/article/43c352f9d77447a8b416c96995ea3831
Autor:
Tom Hilton, Josephine B. Smit, Trevor Jones, Joseph Mwalugelo, Kim Lim, Andrew Seidl, Kelly W. Jones, Brett Bruyere, Jonathan Salerno
Publikováno v:
Conservation Science and Practice, Vol 6, Iss 12, Pp n/a-n/a (2024)
Abstract Cost benefit analysis (CBA) is used in many fields to ensure efficient allocation of scarce resources but is rarely applied in conservation. By using a common metric to evaluate projects in complex social‐ecological systems, CBA can help t
Externí odkaz:
https://doaj.org/article/c33df019818d43a69f90af3db5b5de84