Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Annotation quality is a major bottleneck in building reliable and explainable artificial intelligence (XAI) systems for mental health research. In depression-related datasets, labels are often assigned without structured evidence, symptom-level justification, or traceable alignment with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR), limiting both transparency and downstream model interpretability. We propose a self-evolving, expert-in-the-loop annotation framework for Major Depressive Disorder (MDD) that combines large langua
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- PossiblePossibly related (embedding) · 53%Towards AI-augmented decision making in psychiatry →
- PossiblePossibly related (embedding) · 47%How artificial intelligence is changing the mental health space - southernminn.com →
- PossiblePossibly related (embedding) · 46%How artificial intelligence is changing the mental health space - Nonstop Local News →
- PossiblePossibly related (embedding) · 46%How artificial intelligence is changing the mental health space - The Grand Junction Daily Sentinel →
- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- LinkedLinked via arxiv author · 85%Hoang-Loc Cao →
“Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation”
- LinkedLinked via arxiv author · 85%Van Pham →
“Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation”
- LinkedLinked via arxiv author · 85%Truong Thanh Hung Nguyen →
“Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation”