no code implementations • 27 Feb 2024 • Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally.
no code implementations • 26 Feb 2024 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy
This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials.
2 code implementations • 15 Aug 2023 • Shuangning Li, Lluis Salvat Niell, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Susan Murphy
This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support.
1 code implementation • 15 Aug 2022 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
Dental disease is one of the most common chronic diseases despite being largely preventable.
1 code implementation • 8 Jun 2022 • Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.
no code implementations • 1 Nov 2021 • Supriya Nagesh, Alexander Moreno, Stephanie M. Carpenter, Jamie Yap, Soujanya Chatterjee, Steven Lloyd Lizotte, Neng Wan, Santosh Kumar, Cho Lam, David W. Wetter, Inbal Nahum-Shani, James M. Rehg
The transformer model achieves a non-response prediction AUC of 0. 77 and is significantly better than classical ML and LSTM-based deep learning models.
no code implementations • NeurIPS 2020 • Alexander Moreno, Zhenke Wu, Jamie Yap, David Wetter, Cho Lam, Inbal Nahum-Shani, Walter Dempsey, James M. Rehg
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
1 code implementation • 31 Oct 2018 • Nicholas J. Seewald, Kelley M. Kidwell, Inbal Nahum-Shani, Tianshuang Wu, James R. McKay, Daniel Almirall
We show that the sample size formula for a SMART can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a repeated-measures analysis, and an inflation factor that accounts for the design of a SMART.
Methodology