Unleashing the Potential of Reinforcement Learning for Personalizing Behavioral Transformations with Digital Therapeutics: A Systematic Literature Review

Thure Weimann, Carola Gißke

2024

Abstract

Digital Therapeutics (DTx) are typically considered as patient-facing software applications delivering behavior change interventions to treat non-communicable diseases (e.g., cardiovascular diseases, obesity, diabetes). In recent years, they have successfully developed into a new pillar of care. A central promise of DTx is the idea of personalizing medical interventions to the needs and characteristics of the patient. The present literature review sheds light on using reinforcement learning, a subarea of machine learning, for personalizing DTx-delivered care pathways via self-learning software agents. Based on the analysis of 36 studies, the paper reviews the state of the art regarding the used algorithms, the objects of personalization, evaluation methods, and metrics. In sum, the results highlight the potential and could already demonstrate the medical efficacy. Implications for practice and future research are derived and discussed in order to bring self-learning DTx applications one step closer to everyday care.

Paper Citation

in Harvard Style

Weimann T. and Gißke C. (2024). Unleashing the Potential of Reinforcement Learning for Personalizing Behavioral Transformations with Digital Therapeutics: A Systematic Literature Review. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 230-245. DOI: 10.5220/0012474700003657

in Bibtex Style

@conference{healthinf24,
author={Thure Weimann and Carola Gißke},
title={Unleashing the Potential of Reinforcement Learning for Personalizing Behavioral Transformations with Digital Therapeutics: A Systematic Literature Review},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={230-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012474700003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Unleashing the Potential of Reinforcement Learning for Personalizing Behavioral Transformations with Digital Therapeutics: A Systematic Literature Review
SN - 978-989-758-688-0
AU - Weimann T.
AU - Gißke C.
PY - 2024
SP - 230
EP - 245
DO - 10.5220/0012474700003657
PB - SciTePress