Zobrazeno 1 - 10
of 478
pro vyhledávání: '"Trella, A."'
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
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
We consider the stochastic multi-armed bandit problem with non-stationary rewards. We present a novel formulation of non-stationarity in the environment where changes in the mean reward of the arms over time are due to some unknown, latent, auto-regr
Externí odkaz:
http://arxiv.org/abs/2402.03110
Autor:
Simone Bohnert, Christoph Reinert, Stefanie Trella, Andrea Cattaneo, Ulrich Preiß, Michael Bohnert, Johann Zwirner, Andreas Büttner, Werner Schmitz, Benjamin Ondruschka
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Traumatic brain injury (TBI) is a ubiquitous, common sequela of accidents with an annual prevalence of several million cases worldwide. In forensic pathology, structural proteins of the cellular compartments of the CNS in serum and cerebrosp
Externí odkaz:
https://doaj.org/article/7252679f5bfa4c0b971ccabdcbe627a7
Autor:
Trella, Anna L., Zhang, Kelly W., Nahum-Shani, Inbal, Shetty, Vivek, Doshi-Velez, Finale, Murphy, Susan A.
Dental disease is one of the most common chronic diseases despite being largely preventable. However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and pe
Externí odkaz:
http://arxiv.org/abs/2208.07406
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Autor:
Trella, Anna L., Zhang, Kelly W., Nahum-Shani, Inbal, Shetty, Vivek, Doshi-Velez, Finale, Murphy, Susan A.
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensurin
Externí odkaz:
http://arxiv.org/abs/2206.03944
Within epidemiological modeling, the majority of analyses assume a single epidemic process for generating ground-truth data. However, this assumed data generation process can be unrealistic, since data sources for epidemics are often aggregated acros
Externí odkaz:
http://arxiv.org/abs/2106.10610