用可解释大模型分析社交媒体，揭示慢性病患者心理问题演化规律。
Exploring Complex Mental Health Symptoms via Classifying Social Media Data with Explainable LLMs
- 训练大模型识别社交媒体中的心理健康风险信号
- 首次实现对莱姆病患者未来注意力缺陷风险的预测
- 可视化解释结果，帮助理解心理疾病演进路径
我们提出一个分析复杂疾病的新流程：在具有挑战性的社交媒体文本分类任务上训练大模型，获取分类结果的可解释性分析，并进行定性和定量研究。初步结果显示，该方法能有效预测报告莱姆病症状者中存在心理健康问题的情况；还可预测焦虑障碍患者未来可能出现注意力缺陷多动障碍（ADHD）的风险，并可视化其预测依据，为理解心理疾病演变机制提供新视角。
原文摘要 · Abstract (English)
We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtaining explanations for the classification outputs, and performing qualitative and quantitative analysis on the explanations. We report initial results on predicting, explaining, and systematizing the explanations of predicted reports on mental health concerns in people reporting Lyme disease concerns. We report initial results on predicting future ADHD concerns for people reporting anxiety disorder concerns, and demonstrate preliminary results on visualizing the explanations for predicting that a person with anxiety concerns will in the future have ADHD concerns.
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