在Twitter上使用机器学习探索对青,特拉马多尔和可代因的看法:定量和定性观察性研究
Federico Carabot1,2, Carolina Donat-Vargas3,4,5, Javier Santoma-Vilaclara1,6
1Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain.
Journal of medical Internet research
|November 14, 2023
概括
社交媒体讨论显示,患者优先考虑缓解疼痛的有效性,而不是降醇,可代因和特拉马多尔等止痛药的副作用. 人们担心碎化和缺乏对潜在成风险的认识.
科学领域:
- 药理学 药理学是指药理学的学科.
- 社交媒体分析 社交媒体分析
- 公共卫生 公共卫生
背景情况:
- ,可代因和特拉马多尔是广泛使用的止痛药.
- 他们的非处方药的可用性引起了人们对潜在的阿片类药物成的担忧.
研究的目的:
- 分析推特用户对青醇,可代因和特拉马多尔的看法和经验.
- 识别关于药物的疗效,副作用和非医学内容的讨论趋势.
主要方法:
- 从2019年1月到2020年12月,分析了152,056条英语和西班牙语的推文.
- 根据用户类型,医学内容 (有效性,副作用),科学准确性和非医学主题对推特进行分类.
- 机器学习分类器训练了每种药物1000个手动分类的推文.
主要成果:
- 患者占使用者讨论这些止痛药的73.10%.
- 非医疗内容,包括平化,主导了对弱阿片类药物 (特拉马多尔,可代因) (73.9%) 的讨论.
- 帕拉塞他的讨论显示了医疗内容的更高的流行率 (66.8%),仅限于报告足够的疗效 (6.9%).
结论:
- 患者主要对药物的有效性感兴趣,经常忽视潜在的副作用.
- 社交媒体展示了有关毒品使用趋势的碎化和娱乐性讨论.
- 由于非法销售和无处方购买,监控在线止痛药对话至关重要.
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