Abstract
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.
Original language | English |
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Title of host publication | Machine Learning, Optimization, and Data Science |
Subtitle of host publication | 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part I |
Editors | Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 265-280 |
Number of pages | 16 |
Volume | 1 |
ISBN (Electronic) | 9783030954673 |
ISBN (Print) | 9783030954666 |
DOIs | |
Publication status | Published - 2022 |
Event | 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online Duration: 4 Oct 2021 → 8 Oct 2021 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13163 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 |
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City | Virtual, Online |
Period | 4/10/21 → 8/10/21 |
Bibliographical note
Publisher Copyright:© 2022, Springer Nature Switzerland AG.