The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials

Abstract:Depression is a major mental disorder for which diagnosis relies primarily on clinical assessments. Automated methods to support its detection via the psychiatric MADRS scale are getting more and more attention. While existing solutions primarily focus on detecting the disorder from different text sources (e.g., online text, social media), there is still limited support for clinical trials, where clinical assessments are conducted through structured interviews based on standard guidelines such as SIGMA. In this work, we develop a LLM pipeline specifically designed to support clinicians in supporting the assessment of depression in patients enrolled in clinical trials. Our pipeline converts audio interviews into transcripts, maps them into the ten MADRS symptom items, estimates their severity, and identify problematic clinical ratings associated with them. Evaluation on real clinical interviews shows a strong overall correlation of 0.867 with expert ratings, providing interpretable support for future assessments in clinical trials.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.28190 [cs.CL]

(or arXiv:2607.28190v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.28190 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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