Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition
A two-stage framework separates depression symptom recognition into candidate generation (via contrastively fine-tuned sentence encoder) and definition-grounded verification (via fine-tuned language model) The verification stage cross-checks predictions against candidate-specific diagnostic definitions, improving alignment with clinical standards The pipeline achieves state-of-the-art accuracy and F1 scores across encoder, inference-based, medical, and general LLM baselines Rationales generated
Analysis
TL;DR
- A two-stage framework separates depression symptom recognition into candidate generation (via contrastively fine-tuned sentence encoder) and definition-grounded verification (via fine-tuned language model)
- The verification stage cross-checks predictions against candidate-specific diagnostic definitions, improving alignment with clinical standards
- The pipeline achieves state-of-the-art accuracy and F1 scores across encoder, inference-based, medical, and general LLM baselines
- Rationales generated by the model match expert-authored annotations, with moderate alignment confirmed in preliminary clinical audit
- Performance remains limited for rare symptom categories, highlighting a key area for future improvement
Why It Matters
This work addresses a critical gap in clinical NLP: grounding language model inference in diagnostic definitions rather than relying on surface-level pattern matching. For AI practitioners building healthcare applications, the two-stage decomposition offers a replicable architecture for improving both accuracy and explainability in symptom recognition tasks. The emphasis on definition-grounded verification is particularly relevant as regulatory scrutiny increases on AI systems making clinical decisions.
Technical Details
- Two-stage architecture: Stage 1 uses a contrastively fine-tuned sentence encoder to generate one symptom candidate per sentence; Stage 2 employs a fine-tuned language model to verify presence/absence using the sentence, its context, and a candidate-specific diagnostic definition
- Verification mechanism: The model explicitly checks its judgment against the diagnostic definition before producing a final answer, ensuring reasoning is anchored to clinical criteria rather than statistical correlations alone
- Benchmarking: Evaluated against four baseline categories—encoder-based, inference-based, medical LLMs, and general LLMs—plus a matched single-stage supervised classifier
- Evaluation metrics: Best accuracy and F1 scores among all methods; explanation quality strongly correlated with prediction correctness
- Limitation: Rare symptom categories show degraded performance, suggesting data imbalance remains a bottleneck
Industry Insight
- The candidate-generation-plus-verification paradigm could be generalized to other clinical NLP tasks (e.g., diagnosis coding, adverse event detection) where grounding in definitional criteria is essential for trust and compliance
- Healthcare AI developers should prioritize definition-grounded reasoning over pure accuracy optimization, as regulatory frameworks increasingly demand explainability tied to clinical standards
- Investment in data augmentation strategies for rare symptom categories represents a high-impact research direction, as performance gaps on minority classes could limit real-world clinical deployment
Disclaimer: The above content is generated by AI and is for reference only.