EmoTrack uses LLM clinical signals plus frozen turn-level embeddings and compact cross-session memory to predict PHQ-8 scores, delivering a 13.5% MAE reduction on single-session DAIC-WOZ and competitive results on the new LongCounsel multi-session dataset.
Evaluating large language models for depression symptom estimation
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A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.
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EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes
EmoTrack uses LLM clinical signals plus frozen turn-level embeddings and compact cross-session memory to predict PHQ-8 scores, delivering a 13.5% MAE reduction on single-session DAIC-WOZ and competitive results on the new LongCounsel multi-session dataset.
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AI Models for Depressive Disorder Detection and Diagnosis: A Review
A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.