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Found 8 papers, 4 papers with code
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally.
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in clinical trials.
Dyadic Reinforcement Learning
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.
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Dental disease is one of the most common chronic diseases despite being largely preventable.
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.
Transformers for prompt-level EMA non-response prediction
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.
A Robust Functional EM Algorithm for Incomplete Panel Count Data
Panel count data describes aggregated counts of recurrent events observed at discrete time points.
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome
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
