---
title: "自演化人本框架用于可解释抑郁症状标注"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.15202v1"
permalink: "https://feed.astralor.com/items/itm_57a7b06ca324f7247d302d84"
published: "2026-07-16T16:59:54.000Z"
score: 78
channel: "ai"
tags: ["arXiv","DSM-5-TR","MDD","可解释人工智能","大型语言模型","自我演化框架"]
---

# 自演化人本框架用于可解释抑郁症状标注

## AI 点评

> 这篇论文最值得关注的是它把自演化学习引入到精神健康标注中，通过双记忆架构实现了无需重训练的持续改进。对于从事医疗AI、特别是可解释性和数据集质量控制的研究者来说，这是一个非常实用的工具箱。如果你正在构建抑郁相关的NLP数据集，这篇框架的审计追踪和专家反馈机制值得借鉴。

## 速览

论文提出一种自演化、专家在环的标注框架，用于构建符合DSM-5-TR标准的可解释抑郁症数据集。框架分为候选证据选择、标准级分析和案例级综合三个阶段，采用双记忆架构内化专家反馈，提升标注一致性与可解释性，减少人工修订工作量。初步实验表明该方法有效改善标注质量。

## 关键点

- 提出自演化MDD标注框架，结合LLM辅助与专家验证，生成符合DSM-5-TR的带证据标注。
- 框架包含候选证据选择、标准级分析、案例级综合三阶段，输出临床证据、推理轨迹和编辑历史。
- 双记忆架构（示例记忆与反思记忆）内化专家反馈，实现无需重训练的迭代改进。
- 初步实验显示标注一致性和可解释性提升，人工修订工作量降低。

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_57a7b06ca324f7247d302d84