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pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice
- Source :
- Vrije Universiteit Amsterdam Repository
- Publication Year :
- 2022
-
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.
Details
- Database :
- OAIster
- Journal :
- Vrije Universiteit Amsterdam Repository
- Notes :
- el Hassouni, Ali, Hoogendoorn, Mark, Ciharova, Marketa, Kleiboer, Annet, Amarti, Khadicha, Muhonen, Vesa, Riper, Heleen, Eiben, A. E.
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1375643739
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1007.978-3-030-95467-3_20