acePredicting remission following repetitive transcranial magnetic stimulation in treatment-resistant depression: a comparison of parsimonious and ensemble machine learning models
Repetitive transcranial magnetic stimulation (rTMS) is resource-intensive, and prognostic models may estimate remission probability after treatment in treatment-resistant depression (TRD). Whether algorithmic complexity improves remission prediction over a parsimonious clinical model remains unclear. In a retrospective cohort of 263 adults treated with rTMS who met a baseline data-availability criterion, we compared a simple logistic regression model using age, sex, and baseline MADRS with tuned multivariable re…
Richer feature sets and ensemble methods gave no significant gain in remission discrimination, and discrimination was only moderate, with ROC-AUCs of roughly 0.69 to 0.71.
The cohort was retrospective and only internally validated, and the authors state that the results do not establish equivalence or transportability to other settings.
Evidence
- Peer-reviewedJournal of Affective Disorders2026-10-08
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Truvace Impact Record TRV-2026-1629, v1: “Predicting remission following repetitive transcranial magnetic stimulation in treatment-resistant depression: a comparison of parsimonious and ensemble machine learning models.” Truvace, 2026-10-16. /record/TRV-2026-1629 (accessed at citation time). sha256 9251d185ada08365…
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