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Source note2 min read

Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation

Source

arXiv

arxiv.org/abs/2607.15202v1

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Plain-Language Brief

Researchers propose a self-evolving annotation framework to create explainable and reliable datasets for Major Depressive Disorder (MDD) research. The framework uses a large language model (LLM) in combination with human experts to improve consistency, explanatory power, and reduce manual revision effort.

What the Document Says

  • The paper discusses a self-evolving annotation framework for Major Depressive Disorder (MDD).
  • The framework aims to create explainable, DSM-5-TR-aligned datasets for MDD research, not for clinical diagnosis.
  • It consists of three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations.
  • A dual-memory architecture (Example Memory and Reflection Memory) is designed to internalize expert feedback and improve future annotations without retraining.
  • The framework exports clinical evidence, reasoning traces, and edit histories for comprehensive auditability.
  • In a pilot study, the proposed approach improves annotation consistency, explainability, and reduces manual revision effort.

Key Concepts Mentioned

  • Self-evolving annotation framework
  • Major Depressive Disorder (MDD)
  • DSM-5-TR
  • Large language model (LLM)
  • Expert verification
  • Candidate evidence selection
  • Criterion-level analysis
  • Case-level synthesis
  • Dual-memory architecture
  • Clinical evidence
  • Reasoning traces
  • Edit histories
  • Pilot study

Claims Made by the Document

  • The proposed framework improves annotation consistency and explainability while reducing manual revision effort.

Evidence Used

  • In a pilot study using expert-reviewed samples, the proposed approach shows improvement in annotation consistency and explainability while reducing manual revision effort.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 language model (LLM)-assisted labeling with expert verification. The framework is intended to support the construction of explainable, DSM-5-TR-aligned datasets rather than to perform clinical diagnosis. It operates in three stages: candidate evidence selection from textual records, criterion-level DSM-5-TR analysis, and case-level synthesis that produces label-level diagnostic and severity annotations. A dual-memory architecture, composed of Example Memory and Reflection Memory, is designed to internalize expert feedback and iteratively improve future annotations without retraining. We describe this mechanism and leave its evaluation across multiple feedback cycles to future work. In addition to final labels, the framework exports clinical evidence, reasoning traces, and edit histories, enabling comprehensive auditability. In a pilot study using expert-reviewed samples, the proposed approach improves annotation consistency and explainability while reducing manual revision effort.