Bias in reinforcement learning: A review in healthcare applications
Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
Reinforcement learning for personalization: A systematic literature review
The major application areas of reinforcement learning (RL) have traditionally been game
playing and continuous control. In recent years, however, RL has been increasingly applied …
playing and continuous control. In recent years, however, RL has been increasingly applied …
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 …
Reinforcement learning to send reminders at right moments in smartphone exercise application: A feasibility study
Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies
have indicated that it is an effective strategy in the field of mobile healthcare intervention …
have indicated that it is an effective strategy in the field of mobile healthcare intervention …
Zero-shot recommendations with pre-trained large language models for multimodal nudging
RM Harrison, A Dereventsov… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
We present a method for zero-shot recommendation of multimodal non-stationary content
that leverages recent advancements in the field of generative AI. We propose rendering …
that leverages recent advancements in the field of generative AI. We propose rendering …
RETRACTED ARTICLE: Hybrid firefly with differential evolution algorithm for multi agent system using clustering based personalization
M Anuradha, V Ganesan, S Oliver… - Journal of Ambient …, 2021 - Springer
Abstract Multi-Agent System (MAS) appears to be an efficient, low cost, flexible, and reliable
form of system, these features turns the MAS as a perfect solution for resolving complicated …
form of system, these features turns the MAS as a perfect solution for resolving complicated …
Exploring clustering techniques for effective reinforcement learning based personalization for health and wellbeing
EM Grua, M Hoogendoorn - 2018 IEEE Symposium Series on …, 2018 - ieeexplore.ieee.org
Personalisation has become omnipresent in society. For the domain of health and wellbeing
such personalisation can contribute to better interventions and improved health states of …
such personalisation can contribute to better interventions and improved health states of …
Optimizing patient-specific medication regimen policies using wearable sensors in Parkinson's disease
Effective treatment of Parkinson's disease (PD) is a continual challenge for healthcare
providers, and providers can benefit from leveraging emerging technologies to supplement …
providers, and providers can benefit from leveraging emerging technologies to supplement …
Adapting reinforcement learning treatment policies using limited data to personalize critical care
M Baucum, A Khojandi… - INFORMS Journal on …, 2022 - pubsonline.informs.org
Reinforcement learning (RL) demonstrates promise for developing effective treatment
policies in critical care settings. However, existing RL methods often require large and …
policies in critical care settings. However, existing RL methods often require large and …
End-to-end personalization of digital health interventions using raw sensor data with deep reinforcement learning
We introduce an end-to-end reinforcement learning (RL) solution for the problem of sending
personalized digital health interventions. Previous work has shown that personalized …
personalized digital health interventions. Previous work has shown that personalized …