Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing

Grua, Eoin Martino; Hoogendoorn, Mark

doi:10.1109/ssci.2018.8628621

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…If this is the case, then trajectories D j from some similar environment M j = M i may prove useful in estimating E π i [G]. One such an approach is based on clustering [54,75,124,191]. Formally, it requires q n groups G ∈ {g 1 , .…”

Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

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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 in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.

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Section: Den Hengst Et Al / Reinforcement Learning For Personalizatimentioning

confidence: 99%

Reinforcement learning for personalization: A systematic literature review

Hengst

Grua

Hassouni

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, k-means clustering and RL were combined to develop policies across similar users for the purpose of learning better policies [24]. Clustering methods were also used to effectively learn personalized RL policies in health and wellbeing [7].…”

Section: Related Workmentioning

confidence: 99%

“…In the pH-RL framework, we mitigate this issue and propose to group users with similar behavior using clustering techniques [9,5]. Clustering algorithms such as Kmedoids and K-means have been shown to perform well on similar problems in e-Health [9,5,7]. We compare the behavioral traces of users consisting of states and rewards, and we use the Dynamic Time Warping (DTW) algorithm to calculate the distance between two users.…”

Section: Preliminaries and Problem Statementmentioning

confidence: 99%

pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice

Hassouni¹,

Hoogendoorn²,

Čihařová³

et al. 2022

Machine Learning, Optimization, and Data Science

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While reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life scenarios successfully. This paper presents pH-RL (personalization in e-Health with RL), a general RL architecture for personalization to bring RL to health practice. pH-RL allows for various levels of personalization in health applications and allows for online and batch learning. Furthermore, we provide a general-purpose implementation framework that can be integrated with various healthcare applications. We describe a step-by-step guideline for the successful deployment of RL policies in a mobile application. We implemented our open-source RL architecture and integrated it with the MoodBuster mobile application for mental health to provide messages to increase daily adherence to the online therapeutic modules. We then performed a comprehensive study with human participants over a sustained period. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data. Furthermore, we empirically demonstrate the stability of the learned policies during the study.

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“…A great number of existing proposals already combine different machine learning techniques in order to monitor the health condition of the patient [33][34][35][36][37][38] and provide personalized interactions. In this sense, we have seen systems using techniques such as fuzzy classifiers, artificial neural networks [34,[37][38][39], reinforcement learning [40,41], among others. However, in the context of CORD management, this monitorization is mainly performed with the goal to analyse patient data and detect respiratory diseases or respiratory complications such as exacerbations [36][37][38] rather than understanding the profile (and associated behaviours) of the patient and anticipating further complications.…”

Section: Personal Health Empowermentmentioning

confidence: 99%

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases

et al. 2021

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Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease. KeywordsUser Modelling . Personalized coaching . Mobile health . Preventive healthcare . Healthcare management systems This article is part of the Topical Collection on Mobile & Wireless Health

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Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing

Cited by 10 publications

References 10 publications

Reinforcement learning for personalization: A systematic literature review

Reinforcement learning for personalization: A systematic literature review

pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases

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