The system reduces supervisory triage latency from 72 hours to real time (~10 seconds per session), enabling proactive intervention in high-risk cases and addresses the cold-start problem through Bayesian priors and implements timestamp-based modality synchronization for robust multi-modal fusion.
Abstract
Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a"supervision gap"where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated"Supervisor-in-the-Loop"system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model performs a tri-stream analysis: (1) Therapeutic Alliance tracking via semantic adherence, (2) Latent risk prediction using attention-weighted analytics, and (3) Supervisory Triage via a Dynamic Clinical Urgency Index (D-CUI). Our multi-modal VAL (Visual-Acoustic-Linguistic) framework achieves 95% technique identification accuracy [95% CI: 75.1%-99.9%], alliance assessment MAE of 0.105 on a 5-point scale [95% CI: 0.059-0.151], therapeutic fidelity alpha = 0.423, and mean D-CUI of 0.370 [95% CI: 0.322-0.419]. Training converged in 105 steps with 85.2% loss reduction on a single Tesla T4 GPU. The system reduces supervisory triage latency from 72 hours to real time (~10 seconds per session), enabling proactive intervention in high-risk cases. The system addresses the cold-start problem through Bayesian priors and implements timestamp-based modality synchronization for robust multi-modal fusion.
This paper presents a system for the CLPsych 2026 Shared Task on longitudinal mental health modeling from social media timelines, grounded in the MIND framework, achieving the top Fit and Specificity scores in Task 3.2, demonstrating the benefits of explicit clinical grounding for conceptual accuracy.
Pawan Kumar, Ankit Meshram, S. Jha et al.· Workshop on Computational Li...· 1 citation
G-CARL, a grounded, checklist-aligned reinforcement learning framework that combines multi-source retrieval for atomic claim verification with context-aware, instance-specific weighted checklists for response coverage, providing structured supervision for factuality, user-demand satisfaction, and expression quality without constraining response diversity is proposed.
Shiao Xie, Siyu Chen, Jianwei Lv et al.· 0 citations
Anian is presented, a safety-gated multimodal AI backend for perinatal mental-health support and mindfulness-intervention routing that supports the internal feasibility of the label framework and gating logic but does not establish clinical validity, diagnostic accuracy, real-world safety, or effectiveness.
The results demonstrate that the integration of rich contextual embeddings from BERT with transparent XAI produces a trustworthy, accurate, and actionable system for mental health risk monitoring.
Divya N., V. J. Chakravarthy, K. Jayabharathi et al.· International journal of com...· 0 citations
This Attention Deficit Hyperactivity Disorder (ADHD) remains substantially under-diagnosed among university students despite affecting 2–8% of this population. Campus health services, facing persistent resource constraints, frequently accumulate assessment backlogs of 6–12 months. This paper presents a machine learning framework for automated ADHD pre-screening that combines structured psychometric assessments with natural language processing (NLP)-derived features extracted from free-text clinical self-reports. Drawing on 506 university student responses, we engineer 124 multimodal features spanning four validated instruments, the Adult ADHD Self-Report Scale (ASRS), Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI-II), and Adult Attachment Scale (AAS), together with unstructured diagnostic text. Mutual Information-based feature selection reduces dimensionality to 20 features, yielding a 2% accuracy gain. A comparative evaluation across five classifiers reveals Logistic Regression as the top performer, achieving 81.4% accuracy and an AUC of 0.881. SHAP (SHapley Additive exPlanations) analysis confirms clinical meaningfulness by identifying BAI Item 8 (somatic anxiety), ASRS inattention items, and prior mental health history as the principal risk factors. The system is deployed as an interactive web application that delivers calibrated risk assessments suited to clinical triage in resource limited settings.
It is argued that AI should be framed as an augmentation of - not a replacement for - the clinical relationship, with equity, consent and explainability treated as first-order design constraints.
Dr. B. Anand, Poornima Ramachandran, Abhishek Subramaniam· Proceedings of the Internati...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
