用可解释AI识别多囊卵巢综合征患者的饮食障碍三重负担
When PCOS Meets Eating Disorders: An Explainable AI Approach to Detecting the Hidden Triple Burden
- 基于小模型微调，结合低秩适配生成带文本证据的结构化解释
- 在150条测试帖中达到75.3%精确匹配准确率，有效检测共病情况
- 适合临床筛查辅助，尤其适用于社交媒体中的隐性症状发现
多囊卵巢综合征（PCOS）女性面临体像困扰、进食障碍和代谢问题的多重风险，但现有自然语言处理方法缺乏透明性且无法识别共病表现。本文构建了小型开源语言模型，通过低秩适配微调，在六个Reddit子版块收集的1000条PCOS相关帖子上自动检测该三重负担，并生成基于文本证据的结构化解释。两名训练标注员依据Lee等（2017）临床框架进行标注。对比Gemma-2-2B、Qwen3-1.7B和DeepSeek-R1-Distill-Qwen-1.5B三个模型，最佳模型在150条预留测试帖中取得75.3%的精确匹配准确率，具备稳健的共病检测能力与强可解释性。性能随诊断复杂度下降，表明其更适合用于筛查而非自主诊断。
原文摘要 · Abstract (English)
Women with polycystic ovary syndrome (PCOS) face substantially elevated risks of body image distress, disordered eating, and metabolic challenges, yet existing natural language processing approaches for detecting these conditions lack transparency and cannot identify co-occurring presentations. We developed small, open-source language models to automatically detect this triple burden in social media posts with grounded explainability. We collected 1,000 PCOS-related posts from six subreddits, with two trained annotators labeling posts using guidelines operationalizing Lee et al. (2017) clinical framework. Three models (Gemma-2-2B, Qwen3-1.7B, DeepSeek-R1-Distill-Qwen-1.5B) were fine-tuned using Low-Rank Adaptation to generate structured explanations with textual evidence. The best model achieved 75.3 percent exact match accuracy on 150 held-out posts, with robust comorbidity detection and strong explainability. Performance declined with diagnostic complexity, indicating their best use is for screening rather than autonomous diagnosis.
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