用大模型分析抑郁症患者在Reddit上的用药感受，发现氯胺酮更受好评。
LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Reddit
- 用LLM增强数据，微调DeBERTa实现药物情感分析
- 72.1%用药提及中性，14.8%负面，13.1%正面
- 氯胺酮类药物患者评价显著优于传统抗抑郁药
治疗抵抗型抑郁症（TRD）是多种规范治疗无效的严重抑郁障碍。现有药物证据有限，且临床试验常忽略患者主观耐受性。大规模在线互助叙事为真实世界用药体验提供了补充视角。本研究从28个心理健康类Reddit子版块（2010–2025）中收集了5,059篇明确提及TRD的帖子，涉及3,480名用户。其中3,839篇提及至少一种药物，经词典归一化后共获得23,399条对81种通用名药物的提及。通过基于LLM的数据增强，微调DeBERTa-v3在SMM4H 2023任务上达到0.800的micro-F1。应用于Reddit数据，量化了药物在积极、中性、消极三类情感中的分布，按药物、用户、子版块和年份追踪趋势。总体上，72.1%的提及为中性，14.8%为负面，13.1%为正面。传统抗抑郁药（尤其SSRIs/SNRIs）负面占比更高，而氯胺酮与右旋氯胺酮则表现出更优的情感倾向。结果表明，经归一化的药物提取结合方面感知情感分析，可有效刻画TRD患者群体的真实治疗体验，补充临床证据。
原文摘要 · Abstract (English)
Treatment-resistant depression (TRD) is a severe form of major depressive disorder in which patients do not achieve remission despite multiple adequate treatment trials. Evidence across pharmacologic options for TRD remains limited, and trials often do not fully capture patient-reported tolerability. Large-scale online peer-support narratives therefore offer a complementary lens on how patients describe and evaluate medications in real-world use. In this study, we curated a corpus of 5,059 Reddit posts explicitly referencing TRD from 3,480 subscribers across 28 mental health-related subreddits from 2010 to 2025. Of these, 3,839 posts mentioned at least one medication, yielding 23,399 mentions of 81 generic-name medications after lexicon-based normalization of brand names, misspellings, and colloquialisms. We developed an aspect-based sentiment classifier by fine-tuning DeBERTa-v3 on the SMM4H 2023 therapy-sentiment Twitter corpus with large language model based data augmentation, achieving a micro-F1 score of 0.800 on the shared-task test set. Applying this classifier to Reddit, we quantified sentiment toward individual medications across three categories: positive, neutral, and negative, and tracked patterns by drug, subscriber, subreddit, and year. Overall, 72.1% of medication mentions were neutral, 14.8% negative, and 13.1% positive. Conventional antidepressants, especially SSRIs and SNRIs, showed consistently higher negative than positive proportions, whereas ketamine and esketamine showed comparatively more favorable sentiment profiles. These findings show that normalized medication extraction combined with aspect-based sentiment analysis can help characterize patient-perceived treatment experiences in TRD-related Reddit discourse, complementing clinical evidence with large-scale patient-generated perspectives.
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