← 研踪 PaperTrailarXiv:2605.22286 · v3
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
首次提交 2026-05-21 18:37
版本更新 2026-11-04 22:41
雷达观测 2026-11-05 12:13
摘要
Text-based counseling is widely adopted across online mental health platforms, where automatically estimating depressive symptom severity from transcripts is vital for client safety and clinical monitoring. We therefore study prediction of the standard eight-item Patient Health Questionnaire (PHQ-8) total score directly from counseling sessions. Existing clinical-based methods obtain structured assessments through large language model inference but summarize away individual client turns. Training-based methods preserve turn semantics but must learn clinical structure from limited labels. Integrating these representations requires preserving clinical-feature identity and selecting evidence relevant to each symptom. We propose EmoTrack, which jointly encodes clinical-feature tokens and frozen client-turn embeddings, then uses PHQ-8 item queries to estimate total severity. Optional gated memory incorporates evidence from the preceding session. To support controlled evaluation in the multi-session setting, we introduce CounselArc, a synthetic counseling dataset with session-level PHQ-8 supervision for predicting current-session severity with optional prior-session context. Across real DAIC-WOZ interviews, transfer to E-DAIC, and three longitudinal datasets, EmoTrack achieves the lowest normalized aggregate error. It reduces DAIC-WOZ mean absolute error by 12.5% and aggregate error by approximately 8.6%, each relative to the strongest baseline for that measure.
作者备注：36 pages. Related LongCounsel-8 benchmark: arXiv:2609.03507
观测到的版本
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v3 · 2026-11-04 22:41 · 最新观测版本
EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation
Text-based counseling is widely adopted across online mental health platforms, where automatically estimating depressive symptom severity from transcripts is vital for client safety and clinical monitoring. We therefore study prediction of the standard eight-item Patient Health Questionnaire (PHQ-8) total score directly from counseling sessions. Existing clinical-based methods obtain structured assessments through large language model inference but summarize away individual client turns. Training-based methods preserve turn semantics but must learn clinical structure from limited labels. Integrating these representations requires preserving clinical-feature identity and selecting evidence relevant to each symptom. We propose EmoTrack, which jointly encodes clinical-feature tokens and frozen client-turn embeddings, then uses PHQ-8 item queries to estimate total severity. Optional gated memory incorporates evidence from the preceding session. To support controlled evaluation in the multi-session setting, we introduce CounselArc, a synthetic counseling dataset with session-level PHQ-8 supervision for predicting current-session severity with optional prior-session context. Across real DAIC-WOZ interviews, transfer to E-DAIC, and three longitudinal datasets, EmoTrack achieves the lowest normalized aggregate error. It reduces DAIC-WOZ mean absolute error by 12.5% and aggregate error by approximately 8.6%, each relative to the strongest baseline for that measure.
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