Byeon H. Can reinforcement learning effectively prevent depression relapse? World J Psychiatry 2025; 15(8): 106025 [DOI: 10.5498/wjp.v15.i8.106025]
Corresponding Author of This Article
Haewon Byeon, PhD, Associate Professor, Worker’s Care & Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, No. 1600 Chungjeol-ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Aug 19, 2025; 15(8): 106025 Published online Aug 19, 2025. doi: 10.5498/wjp.v15.i8.106025
Can reinforcement learning effectively prevent depression relapse?
Haewon Byeon
Haewon Byeon, Worker’s Care & Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, Cheonan 31253, South Korea
Author contributions: Byeon H contributed to data interpretation, developed methodology, and manuscript writing.
Supported by the Education and Research Promotion Program of KOREATECH.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, PhD, Associate Professor, Worker’s Care & Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, No. 1600 Chungjeol-ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Received: February 14, 2025 Revised: April 7, 2025 Accepted: June 18, 2025 Published online: August 19, 2025 Processing time: 176 Days and 3.1 Hours
Abstract
Depression is a prevalent mental health disorder characterized by high relapse rates, highlighting the need for effective preventive interventions. This paper reviews the potential of reinforcement learning (RL) in preventing depression relapse. RL, a subset of artificial intelligence, utilizes machine learning algorithms to analyze behavioral data, enabling early detection of relapse risk and optimization of personalized interventions. RL's ability to tailor treatment in real-time by adapting to individual needs and responses offers a dynamic alternative to traditional therapeutic approaches. Studies have demonstrated the efficacy of RL in customizing e-Health interventions and integrating mobile sensing with machine learning for adaptive mental health systems. Despite these advantages, challenges remain in algorithmic complexity, ethical considerations, and clinical implementation. Addressing these issues is crucial for the successful integration of RL into mental health care. This paper concludes with recommendations for future research directions, emphasizing the need for larger-scale studies and interdisciplinary collaboration to fully realize RL’s potential in improving mental health outcomes and preventing depression relapse.
Core Tip: Reinforcement learning (RL) holds significant promise in preventing depression relapse by enabling personalized and adaptive mental health interventions. By leveraging advanced machine learning algorithms, RL can analyze behavioral data for early relapse risk detection and optimize treatment strategies tailored to individual needs. This study reviews the existing literature, highlighting RL’s potential to transform mental health care through personalized learning and data-driven decision-making. However, challenges such as algorithmic complexity and ethical considerations must be addressed. Future research should focus on larger-scale studies and interdisciplinary collaboration to establish RL as a viable tool for effective depression management and relapse prevention.
