pH-RL: A personalization architecture to bring reinforcement learning to health practice

• URL: http://arxiv.org/abs/2103.15908v2
• Date: Wed, 31 Mar 2021 00:46:39 GMT
• Title: pH-RL: A personalization architecture to bring reinforcement learning to health practice
• Authors: Ali el Hassouni, Mark Hoogendoorn, Marketa Ciharova, Annet Kleiboer, Khadicha Amarti, Vesa Muhonen, Heleen Riper, A. E. Eiben
• Abstract summary: This paper presents pH-RL, a general RL architecture for personalization to bring RL to health practice. We implement our open-source RL architecture and integrate it with the MoodBuster mobile application for mental health. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data.
• Score: 6.587485396428361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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