Can reinforcement learning effectively prevent depression relapse?

Table 1 Summary of research studies

Ref.	Algorithm/model	Target subjects	Intervention program	Main results
Lindqvist et al[11]	RL	Psychiatric disorders patients	Not specified	Examines underlying mechanisms linking mental illness with cellular aging through reinforcement learning
Pike and Robinson[12]	RL in mood disorders	Patients with mood/anxiety disorders	Meta-analysis	Identifies differences in RL patterns between mood disorders and control individuals
Crawley[13]	Computation and RL framework	Patients with depression	Behavioral activation	Highlights the role of RL in understanding changes in psychological therapy for depression
Weimann and Gißke[14]	Digital therapeutics with RL	General healthcare consumers	Personalizing behavioral transformations through digital platforms	Reviews potential of RL in personalizing therapy with gamified engagement tools
Petrescu et al[15]	RL in VR environments	Anxiety disorder patients	VR with biosignals integration	Uses VR environments with RL algorithms for anxiety detection and management
Guan et al[16]	Affective BCI	Adolescents	Neurofeedback and RL	Implements RL with BCIs to modulate brain activity and detect depression in adolescence
Kunisato et al[17]	Probabilistic RL	Patients with depression	Reward-based decision-making tasks	Analyzes effects of depression on reward-based decisions and action variability
Yoon et al[18]	Tree-based RL	Patients with hand deformity	Personalized treatment decisions	Applies RL to determine optimal personalized treatments in rheumatoid arthritis

RL: Reinforcement learning; VR: Virtual reality; BCIs: Brain-computer interfaces.