Reinforcement learning in healthcare: A survey
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …
making by using interaction samples of an agent with its environment and the potentially …
Reinforcement learning for intelligent healthcare applications: A survey
Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
Notice of retraction: AI techniques for COVID-19
Notice of Retraction "AI Techniques for COVID-19," by Adedoyin Ahmed Hussain; Ouns
Bouachir; Fadi Al-Turjman; Moayad A Page 1 Notice of Retraction "AI Techniques for COVID-19," …
Bouachir; Fadi Al-Turjman; Moayad A Page 1 Notice of Retraction "AI Techniques for COVID-19," …
[HTML][HTML] Artificial intelligence and obesity management: an obesity medicine association (OMA) clinical practice statement (CPS) 2023
HE Bays, A Fitch, S Cuda, S Gonsahn-Bollie, E Rickey… - Obesity Pillars, 2023 - Elsevier
Abstract Background This Obesity Medicine Association (OMA) Clinical Practice Statement
(CPS) provides clinicians an overview of Artificial Intelligence, focused on the management …
(CPS) provides clinicians an overview of Artificial Intelligence, focused on the management …
Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity
With the recent proliferation of mobile health technologies, health scientists are increasingly
interested in develo** just-in-time adaptive interventions (JITAIs), typically delivered via …
interested in develo** just-in-time adaptive interventions (JITAIs), typically delivered via …
Early detection of depression using a conversational AI bot: A non-clinical trial
Background Artificial intelligence (AI) has gained momentum in behavioural health
interventions in recent years. However, a limited number of studies use or apply such …
interventions in recent years. However, a limited number of studies use or apply such …
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. Common challenges in …
interventions in the fields of mobile health and online education. Common challenges in …
Optimizing adaptive notifications in mobile health interventions systems: reinforcement learning from a data-driven behavioral simulator
Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with
users. Instead of designing such complex strategies manually, reinforcement learning (RL) …
users. Instead of designing such complex strategies manually, reinforcement learning (RL) …
[HTML][HTML] Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions
A Giovanelli, J Rowe, M Taylor, M Berna… - Journal of medical …, 2023 - jmir.org
Understanding and optimizing adolescent-specific engagement with behavior change
interventions will open doors for providers to promote healthy changes in an age group that …
interventions will open doors for providers to promote healthy changes in an age group that …
Intelligentpooling: Practical thompson sampling for mhealth
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time
to a user with the goal of hel** the user adopt and maintain healthy behaviors …
to a user with the goal of hel** the user adopt and maintain healthy behaviors …