Group-Driven Reinforcement Learning for Personalized mHealth Intervention

Zhu, Fayan; Guo, Jun; Xu, Zheng; Liao, Peng; Liu, Yang; Huang, Junzhou

doi:10.1007/978-3-030-00928-1_67

Cited by 13 publications

(13 citation statements)

References 21 publications

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“…On a different note, RL has been applied to personalized healthcare. In particular, Zhu et al [134] introduces group-driven RL in personalized healthcare, taking into considerations different groups, each having similar agents. As usual, Q-value is optimized with respect to policy π θ , which can be qualitatively interpreted as the maximization of rewards over time over the choices of action selected by many participating agents in the system.…”

Section: B Interpretability Via Mathematical Structure 1) Predefinedmentioning

confidence: 99%

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa

Guan

2021

IEEE Trans. Neural Netw. Learning Syst.

1,094

547

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Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

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Section: B Interpretability Via Mathematical Structure 1) Predefinedmentioning

confidence: 99%

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa

Guan

2021

IEEE Trans. Neural Netw. Learning Syst.

1,094

547

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show abstract

“…Similar work with the group-driven RL is proposed for health care on a mobile device for personalized mHealth Intervention. In this work, K-means clustering is applied for grouping the people and finally shared with RL policy for each group [285]. Optimal policy learning is a challenging task with RL for an agent.…”

mentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

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In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

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“…The latter also shows several forms of personalization that result from learning from patients with different (scheduling) habits. The work by Zhu et al(2017) is related to ours, in that they too focus on clustering the set of users for personalization purposes and use a form of linear function approximation based batch learning as part of their approach. In addition to algorithmic differences in learning but also in clustering, a major difference is that we base our experiments on extensive runs with our novel simulator.…”

Section: Related Workmentioning

confidence: 99%

“…(Taylor & Stone, 2009)) or (2) pool data from multiple users that require similar policies (cf. (Zhu et al, 2017)). While both are viable options, the latter one has not been explored for more complex and realistic health settings yet, merely for very simple personalization settings.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present a cluster based reinforcement learning algorithm which builds on top of the work done by (Zhu et al, 2017) and test it for a more complex health setting using a dedicated simulator we have built. We use k-means clustering to find suitable clusters, thereby automatically selecting a value for k using the silhouette score.…”

Section: Introductionmentioning

confidence: 99%

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Personalization of Health Interventions Using Cluster-Based Reinforcement Learning

Hassouni

Hoogendoorn

Otterlo

et al. 2018

Lecture Notes in Computer Science

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Research has shown that personalization of health interventions can contribute to an improved effectiveness. Reinforcement learning algorithms can be used to perform such tailoring using data that is collected about users. Learning is however very fragile for health interventions as only limited time is available to learn from the user before disengagement takes place, or before the opportunity to intervene passes. In this paper, we present a cluster-based reinforcement learning approach which learns across groups of users. Such an approach can speed up the learning process while still giving a level of personalization. The clustering algorithm uses a distance metric over traces of states and rewards. We apply both online and batch learning to learn policies over the clusters and introduce a publicly available simulator which we have developed to evaluate the approach. The results show batch learning clearly outperforms online learning. Furthermore, clustering can be beneficial provided that a proper clustering is found.

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Group-Driven Reinforcement Learning for Personalized mHealth Intervention

Cited by 13 publications

References 21 publications

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Personalization of Health Interventions Using Cluster-Based Reinforcement Learning

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