在整个COVID-19大流行期间,在Reddit上探索炎症性肠病话语使用OpenAI的GPT-3.5Turbo模型:分类模型验证和案例研究
Tyler Babinski1,2, Sara Karley3, Marita Cooper4
1Division of Gastroenterology, Hepatology, and Nutrition, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
Journal of medical Internet research
|July 3, 2025
概括
在线炎性肠病 (IBD) 患者社区是有价值的. 使用GPT-3.5 Turbo对Reddit帖子的分析表明,药物和症状是关键话题,中立情绪主导了年轻人之间的讨论.
科学领域:
- 在医疗保健中的自然语言处理 (NLP)
- 社交媒体分析用于患者洞察力.
- 医疗研究中的人工智能 (AI)
背景情况:
- 炎症性肠病 (IBD) 是一种慢性自身免疫性疾病,患病率正在上升.
- 在线患者社区对于IBD支持至关重要,特别是在COVID-19大流行期间.
- 在这些在线空间中,患者与患者之间的沟通是研究不足的.
研究的目的:
- 分析来自主要Reddit IBD支持组的社区帖子 (2020年3月 - 2022年12月).
- 使用预训练的变压器模型 (OpenAI的 GPT-3.5 Turbo) 来进行数据分类.
- 为了验证人工智能驱动的分类与人类评分的准确性.
主要方法:
- 收集了53,333个帖子从r/CrohnsDisease,r/性结肠炎和r/IBD.
- 使用GPT-3.5 Turbo进行情绪,主题,人口统计和COVID-19提及的分类帖子.
- 手动评分了397个帖子,以评估与AI模型的互评分协议.
主要成果:
- 人工智能和人类评级者之间达成高度一致 (Fleiss κ 0.53-0.91),对于性别来说几乎完美.
- 药物和症状是最常见的主题;中立的情绪是最常见的.
- 在网上讨论IBD的用户倾向于年轻 (20-39岁),并且与一般IBD人口相比,性别分布不同.
结论:
- 产生性预训练过的变压器模型有效地从医疗社交媒体数据中提取见解.
- 经过验证的数据集可以支持对IBD患者体验的进一步子分析.
- 像GPT-3.5 Turbo这样的AI模型可以应用于分析各种健康状况的社交媒体数据.
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