发表机构
The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将经前症状追踪转化为对话式有序标签恢复问题，用ModernBERT和Qwen2.5-1.5B-Instruct模型，实现低负担的日常经前症状评分，三簇策略效果接近固定六项策略且提问量减半。
AI 中文摘要
前瞻性每日症状追踪是经前健康评估的核心，但重复的有序量表会带来极大的应答负担。我们将对话式管理构建为一个有序标签恢复问题：系统主动引出一小组症状簇，并将每个应答映射到原始严重程度标签。我们使用了来自mcPHASES数据集的3320个完整参与者-天数据，涵盖痉挛、情绪波动、疲劳、睡眠问题、压力和腹胀，采用六级量表。预留6名参与者用于开发，36名用于冻结评估，包含360个参与者-天和2160个项目标签。ModernBERT证据门检测是否表达了某一症状，Qwen2.5-1.5B-Instruct生成确定性结构化严重程度评分。固定六项提问的二次加权kappa为0.976，而三个联合症状簇提问的二次加权kappa为0.913，在一个严重程度等级内的一致性为97.45%，中度或更高症状的召回率为80.94%，同时减少了50%的提问量。先开放的自适应策略需要3.92-5.98个提问，且产生的一致性低于相应的固定策略。参与者-簇自助分析估计，三簇策略与六项策略之间的kappa差异为-0.062（95%置信区间为-0.076至-0.048）。主动簇级引出提供了一条从自然对话到可重复日常症状标签的直接局部模型路径。
英文摘要
Prospective daily symptom tracking is central to premenstrual health assessment, but repeated ordinal forms impose substantial response burden. We formulate conversational administration as an ordinal label-recovery problem: the system actively elicits a small set of symptom clusters and maps each response to the original severity labels. We used 3,320 complete participant-days from the mcPHASES dataset, covering cramps, mood swing, fatigue, sleep issues, stress, and bloating on a six-level scale. Six participants were reserved for development and 36 for a frozen evaluation comprising 360 participant-days and 2,160 item labels. A ModernBERT evidence gate detected whether a symptom was expressed, and Qwen2.5-1.5B-Instruct produced deterministic structured severity scores. Fixed six-item questioning achieved a quadratic weighted kappa of 0.976, whereas three joint symptom-cluster questions achieved 0.913, 97.45% agreement within one severity level, and 80.94% recall for moderate-or-higher symptoms while reducing questions by 50%. Open-first adaptive policies required 3.92-5.98 questions and produced lower agreement than the corresponding fixed policies. Participant-cluster bootstrap analysis estimated a kappa difference of -0.062 (95% CI -0.076 to -0.048) between the three-cluster and six-item strategies. Active cluster-level elicitation provides a direct, local-model route from natural conversation to reusable daily symptom labels.
Comments10 pages, 4 figures, 1 table
