Assess the Utility of a Speech-based Machine Learning Algorithm to Predict Treatment Response to Psychiatric Interventions
This observational cohort study (n=200) will assess whether a speech-based machine learning algorithm can predict treatment response to psychiatric interventions, specifically repetitive transcranial magnetic stimulation (TMS) and Spravato (esketamine) nasal spray.
Details
This prospective observational cohort (n=200) will enrol outpatients aged 18–68 receiving TMS or Spravato (esketamine) as part of clinical care; speech recordings are collected before treatment, daily during treatment, immediately after treatment, and at 4‑week follow-up.
The primary aim is to train and validate a speech-based machine learning algorithm to predict treatment response, defined as a ≥2-point improvement on the Clinical Global Impression that is sustained through the 4‑week follow-up.
Participants complete ~12‑minute speech recordings responding to six prompts and standard clinical rating scales; the study will also monitor for any distress caused by the speech assessments.