International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 1
January-February 2026

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Adaptive Reinforcement Learning–Based Multimodal Framework for Mental Health Disorder Detection

Author(s) Mr. Alok Mishra
Country India
Abstract Reinforcement learning (RL), a core paradigm within machine learning, has gained increasing attention in mental health research due to its ability to model adaptive decision-making over time. Unlike static predictive approaches, RL frameworks learn from continuous feedback, making them particularly suitable for personalized and dynamic mental health interventions. This review examines the theoretical foundations of reinforcement learning and synthesizes its emerging applications across clinical decision support, digital mental health interventions, and computational psychiatry. It discusses how RL has been used to personalize treatment strategies, model reward and punishment learning in psychiatric disorders, and support adaptive digital therapeutics. Key challenges related to model interpretability, ethical deployment, data availability, and clinical integration are critically analyzed. The review concludes that while reinforcement learning offers substantial promise for advancing personalized mental healthcare, its successful translation into real-world clinical practice will require close collaboration between clinicians, data scientists, and ethicists.
Keywords Reinforcement Learning; Mental Health; Computational Psychiatry; Digital Mental Health; Clinical Decision Support Systems; Artificial Intelligence; Personalized Healthcare
Field Computer
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-01-29
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.64877
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