Reinforcement learning for repetitive transcranial magnetic stimulation protocol selection in treatment-resistant depression

Abstract

Repetitive transcranial magnetic stimulation (rTMS), including intermittent theta-burst stimulation (iTBS), is an established treatment for treatment-resistant depression, but clinicians lack evidence-based guidance for session-level choices among stimulation type, intensity, and laterality. In this study, we investigated whether a reinforcement learning (RL) algorithm trained on routine clinical data could recommend session-level rTMS protocols associated with greater symptom improvement using electronic health record data from 1609 adults treated with rTMS between January 2023 and June 2025 across 19 clinic groups. Among 370 patients from held-out clinics, treatment decisions matching algorithm recommendations were associated with 0.788 points greater next-session Patient Health Questionnaire-9 improvement than treatments that differed (95% CI: 0.634–0.940). Following every recommendation was projected to produce 3.22 points of additional improvement (95% CI: 2.72–3.75). These findings suggest RL can find treatment patterns associated with greater next-session improvement and potentially meaningful improvement accumulated across a treatment episode.