Journal Article10.1016/J.ARTMED.2021.102062
A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions.
25
TL;DR: A reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s) is proposed and a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory is proposed.
read more
About: This article is published in Artificial Intelligence in Medicine. The article was published on 02 Apr 2021. The article focuses on the topics: Reinforcement learning & Transfer of learning.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
TL;DR: A comprehensive review of remote patient monitoring (RPM) systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM is presented in this article .
180
Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator.
TL;DR: In this article, a data-driven approach integrating psychological insights and knowledge of historical data is presented to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing.
A systematic scoping review of just-in-time, adaptive interventions (JITAIs) finds limited automation and incomplete reporting.
TL;DR: In this article , the authors conducted a systematic scoping review of just-in-time, adaptive interventions (JITAIs) assessed in randomized controlled trials (RCTs) in any medical specialty, and assessed the completeness of intervention reporting.
15
Exploring the Feasibility of Using ChatGPT for Creating a Just-in-Time-Adaptive Physical Activity Mobile Health Intervention Content: Case Study (Preprint)
Amanda Willms,Sam Liu +1 more
TL;DR: ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI, but it is essential to approach its use, along with other language models, with caution.
6
Understanding What Drives Long-term Engagement in Digital Mental Health Interventions: Secondary Causal Analysis of the Relationship Between Social Networking and Therapy Engagement
Shaunagh O'Sullivan,Niels van Berkel,Vassilis Kostakos,Lianne Schmaal,Simon D'Alfonso,Lee Benson Valentine,Sarah Bendall,Barnaby Nelson,John Gleeson,Mario Alvarez-Jimenez +9 more
TL;DR: In this paper , the authors investigated the causal relationship between the social network and therapeutic components of Horyzons and found that the social networking aspects of the intervention were the most engaging.
5
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
•Book
Reinforcement Learning: An Introduction
Richard S. Sutton,Andrew G. Barto +1 more
- 01 Jan 1988
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Social Foundations of Thought and Action : A Social Cognitive Theory
Albert Bandura
- 01 Jan 1986
TL;DR: In this article, models of Human Nature and Casualty are used to model human nature and human health, and a set of self-regulatory mechanisms are proposed. But they do not consider the role of cognitive regulators.
38.3K
Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew D. Taylor,Peter Stone +1 more
TL;DR: This article presents a framework that classifies transfer learning methods in terms of their capabilities and goals, and then uses it to survey the existing literature, as well as to suggest future directions for transfer learning work.
How are habits formed: Modelling habit formation in the real world
TL;DR: In this paper, the authors investigated the process of habit formation in everyday life and found that repetition of a behaviour in a consistent context increases automaticity following an asymptotic curve which can be modelled at the individual level.