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使用人工智能分析胰高血糖素样肽-1受体激动剂社交媒体帖子的趋势

Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence.

作者信息

Javaid Aamir, Baviriseaty Sruthika, Javaid Rehan, Zirikly Ayah, Kukreja Harshita, Kim Chang H, Blaha Michael J, Blumenthal Roger S, Martin Seth S, Marvel Francoise A

机构信息

Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Digital Health Innovation Laboratory, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

出版信息

JACC Adv. 2024 Aug 28;3(9):101182. doi: 10.1016/j.jacadv.2024.101182. eCollection 2024 Sep.

Abstract

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have surged in popularity in recent years, with discussions about their on-label and off-label use spilling into the public forum. No study has analyzed online discussions about GLP-1RAs.

OBJECTIVES

The purpose of this study was to analyze perceptions of GLP-1RAs on social media.

METHODS

We analyzed GLP-1RA-related posts on Reddit between May 28, 2013, and June 1, 2023. All posts were identified that included generic or brand names of GLP-1RAs. Post volume on Reddit was compared to search interest on Google over time. An artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model (Bidirectional Encoder Representations from Transformers [BERT]), a dimensionality reduction technique, and a clustering algorithm was used to cluster posts into related topics. Discussion sentiment was classified using a pretrained BERT model and assessed qualitatively.

RESULTS

14,390 GLP-1RA-related Reddit posts by 8,412 authors were identified. Ninety-four percent of posts were created after 2021, consistent with search interest trend on Google. We used the AI model to categorize posts into 30 topics which were hierarchically grouped by the model based on shared content. Posts were identified among communities for individuals with diabetes and obesity, as well as for diseases without a Food and Drug Administration-approved indication. Most posts had a negative sentiment using the pretrained model, acknowledging the pretrained model is at risk for misclassifying posts.

CONCLUSIONS

AI can generate insights on perceptions of GLP-1RAs on social media. Common themes included success stories of improving diabetes and obesity management, struggles with insurance coverage, and questions regarding diet, side effects, and medication administration.

摘要

背景

近年来,胰高血糖素样肽-1受体激动剂(GLP-1RAs)的使用日益普遍,关于其适应证内和适应证外使用的讨论已进入公共论坛。尚无研究分析过关于GLP-1RAs的在线讨论。

目的

本研究旨在分析社交媒体上对GLP-1RAs的看法。

方法

我们分析了2013年5月28日至2023年6月1日期间Reddit上与GLP-1RAs相关的帖子。识别出所有包含GLP-1RAs通用名或品牌名的帖子。随着时间的推移,将Reddit上的帖子数量与谷歌上的搜索兴趣进行比较。使用由半监督自然语言处理模型(来自变换器的双向编码器表示[BERT])、降维技术和聚类算法组成的人工智能(AI)管道将帖子聚类为相关主题。使用预训练的BERT模型对讨论情绪进行分类并进行定性评估。

结果

识别出8412名作者发布的14390条与GLP-1RAs相关的Reddit帖子。94%的帖子是在2021年之后创建的,这与谷歌上的搜索兴趣趋势一致。我们使用AI模型将帖子分类为30个主题,该模型根据共享内容将这些主题进行分层分组。在糖尿病和肥胖症患者群体以及无美国食品药品监督管理局批准适应证的疾病相关群体中识别出了这些帖子。使用预训练模型时,大多数帖子的情绪为负面,同时承认预训练模型存在对帖子分类错误的风险。

结论

人工智能可以生成关于社交媒体上对GLP-1RAs看法的见解。常见主题包括改善糖尿病和肥胖症管理的成功案例、保险覆盖方面的困难以及关于饮食、副作用和药物管理的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/890e/11450939/22167dbf33db/ga1.jpg

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