一种基于人工智能的整体方法,从患者在社交媒体上的行为来优化药监管
Valentin Roche1, Jean-Philippe Robert1, Hanan Salam2
1Université Claude Bernard - Lyon 1, Faculté de Pharmacie, Institut des Sciences Pharmaceutiques et Biologiques, 8 Avenue Rockefeller, 69008, Lyon, France.
Artificial intelligence in medicine
|October 2, 2023
概括
这项研究引入了一种人工智能方法,用于使用社交媒体监测药物安全性. 它随着时间的推移跟踪用户行为,以更有效地检测药物不良事件,达到75%的准确性.
科学领域:
- 人工智能的人工智能
- 药物监督 药物监督 药物监督
- 计算语言学 计算语言学
背景情况:
- 传统的药物监测依赖于自发报告,通常有延迟.
- 社交媒体提供了丰富的实时来源,患者报告的药物体验.
- 监测患者行为演变是早期安全信号检测的关键.
研究的目的:
- 利用社交媒体数据开发基于人工智能的整体药物监测方法.
- 分析用户行为指标的时间演变,以检测不良药物事件 (ADE).
- 通过在患者在线讨论中识别异常周期来优化药物安全监测.
主要方法:
- 利用自然语言处理 (NLP) 技术,包括单词频率,语义相似性和情感分析.
- 根据患者的评论,开发了一种分类方法来区分正常和异常的时间段.
- 提出了一个词云卷积神经网络 (WC-CNN) 用于分类,从患者数据中训练词云.
- 专注于法国的Levothyrox®案例作为现实世界的验证.
主要成果:
- 整体方法,分析用户行为指标随着时间的推移,在优化药物监督方面被证明是有效的.
- 月度时间分辨率和拟议的NLP指标在检测新安全信号方面表现出有效性.
- WC-CNN模型在识别与患者评论相关的异常周期方面达到75%的准确性.
结论:
- 对社交媒体用户行为演变的AI驱动分析为药监提供了一个有希望的,优化的药物监管方法.
- 拟议的WC-CNN模型和NLP指标可以有效地标记潜在的药物安全问题,以便进一步调查.
- 开源代码促进了人工智能在药物安全监测中的更广泛采用和发展.
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