LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives Beyond Rating Scales
This study analyzes de-identified Turkish teacher evaluation forms from clinical ADHD assessments, comparing predictive signals from structured rating scales (CTRS-R:S) and open-ended teacher narratives. The authors find that structured and narrative information encode complementary signals, with minimal overlap between cases missed by each modality. An LLM-assisted theme discovery pipeline reveals distinct attention, behavioral, and family-related patterns in narratives that structured scales miss, demonstrating NLP's potential to augment traditional ADHD screening.
Related guides (2)
Related events (8)
Dep-LLM: Training-free depression diagnosis framework using structured multi-factor LLM reasoning
Dep-LLM is a training-free framework for automatic depression detection from clinical interviews that uses frozen foundation LLMs without fine-tuning. The system decomposes long clinical dialogues into five thematic factors via Chain-of-Thought analysis, applies token-level entropy-based confidence modulation, and integrates multi-factor signals for final diagnosis. Evaluated on DAIC-WOZ and E-DAIC datasets, it outperforms zero-shot baselines across 21 foundation LLMs and surpasses supervised domain-specific and commercial LLMs on multiple metrics.
LLMs predict dementia and depression severity from clinical interview transcripts in zero-shot and feature-extraction settings
Researchers evaluate three open-weights LLMs (Mistral 3.1, DeepHermes, Qwen3) for predicting dementia and depression severity from speech transcripts of 154 German-speaking patients in standardized clinical interviews. The study introduces a new observer-based Global Depression Scale (GDS-D) and tests both zero-shot prediction and LLM-based feature extraction for Support Vector Regression. Zero-shot performs well for depression (MAE 0.60), while structured feature extraction reduces dementia assessment error by up to 35%; pause-enriched automatic transcripts match human transcription quality, suggesting viable fully-automated screening pipelines.
Automated ICD Classification of Psychiatric Diagnoses Using NLP and LLMs
This study evaluates NLP and ML approaches for automating the mapping of free-text psychiatric descriptions to ICD diagnostic codes, using a dataset of 145,513 Spanish clinical records. Methods range from classical BoW/TF-IDF representations to transformer-based embeddings including e5_large, BioLORD, and Llama-3-8B. Fine-tuned e5_large achieved the best performance with a micro-F1 of 0.866, outperforming classical methods by capturing semantic nuance and medical terminology. The work highlights challenges of long-tail label distributions and ambiguity specific to psychiatric clinical language.
Systematic Evaluation of LLM Safety Failures on Eating Disorder Queries with Clinician Feedback
This paper investigates how LLMs respond to queries from users with eating disorders, finding that specific linguistic cues in prompts increase the likelihood of unsafe model responses. Working with clinical ED experts, the authors systematically vary risk levels in user prompts to measure the extent to which LLMs uncritically adapt to potentially dangerous inputs. The study highlights a gap between perceived model safety and actual harm facilitation in sensitive health contexts.
Evidence-Augmented ML for Self-Harm Surveillance in Emergency Department Triage Notes
Researchers developed a three-stage pipeline combining traditional machine learning with LLM-based screening and evidence extraction to detect self-harm in Australian emergency department triage notes. The system achieved AUPRCs around 0.88 in both internal and external validation, and transferred to two external hospital sites without site-specific retraining. A notable capability is identifying the primary self-harm method with 95% accuracy, enabling more granular public health surveillance beyond binary classification.
Adaptive LLM tutoring system with subject-aware prompt routing improves high-school student engagement
Researchers develop and evaluate an LLM-based tutoring system that uses a learned prompt routing model to dynamically select pedagogical strategies based on 14 features extracted from conversation transcripts. The system was trained in simulation and deployed in an A/B test with 359 high-school students (656 conversations), showing sim-to-real transfer and reducing required interactions by ~3 turns. A stochastic routing strategy achieved a notably higher exercise conversion rate (28.1%) compared to a greedy router (19.1%) and static baseline (19.6%).
LLUMI: Fine-Tuning Open-Source LLMs for Mental Health Writing Assistance Using Reddit Community Feedback
LLUMI is a two-component system (a generation model and an improvement model) designed to provide mental health writing assistance using smaller open-source LLMs hosted in privacy-preserving, on-premise environments. The system leverages Reddit community endorsement signals (upvotes/downvotes) to construct preference pairs for SFT and DPO training, then further aligns outputs via human evaluation across readability, empathy, connection, actionability, and safety dimensions. Results show LLUMI achieves performance comparable to proprietary GPT-based models on linguistic and human evaluations, suggesting community-derived preference signals can substitute for expensive expert labeling in sensitive domains.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.