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

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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 languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science - 7th International Conference, LOD 2021, Revised Selected Papers
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton
PublisherSpringer Science and Business Media Deutschland GmbH
Pages265-280
Number of pages16
Volume13163
Edition26
ISBN (Print)9783030954666
DOIs
Publication statusPublished - 2022
Event7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online
Duration: 4 Oct 20218 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13163 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
CityVirtual, Online
Period4/10/218/10/21

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

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