The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials

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.

A Multi-decoder Neural Tracking Method for Accurately Predicting Speech Intelligibility

Objective: EEG-based methods can predict speech intelligibility, but their accuracy and robustness lag behind behavioral tests, which typically show test-retest differences under 1 dB. We introduce the multi-decoder method to predict speech reception thresholds (SRTs) from EEG recordings, enabling objective assessment for populations unable to perform behavioral tests; such as those with disorders of consciousness or during hearing aid fitting. Approach: The method aggregates data from hundreds of decoders, each trained on different speech features and EEG preprocessing setups to quantify neural tracking (NT) of speech signals. Using data from 39 participants (ages 18-24), we recorded 29 minutes of EEG per person while they listened to speech at six signal-to-noise ratios and a quiet story. NT values were combined into a high-dimensional feature vector per subject, and a support vector regression model was trained to predict SRTs from these vectors. Main Result: Predictions correlated significantly with behavioral SRTs (r = 0.647, p < 0.001; NRMSE = 0.19), with all differences under 1 dB. SHAP analysis showed theta/delta bands and early lags had slightly greater influence. Using pretrained subject-independent decoders reduced required EEG data collection to 15 minutes (3 minutes of story, 12 minutes across six SNR conditions) without losing accuracy.