Can reinforcement learning effectively prevent depression relapse?

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.