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在消费者评论中使用自然语言处理来描述和预测顺势疗法产品相关的不良事件:与FDA不良事件报告系统 (FAERS) 的报告进行比较

Karen Konkel^1,2, Nurettin Oner², Abdulaziz Ahmed²

¹Division of Pharmacovigilance, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, United States.

Journal of the American Medical Informatics Association : JAMIA

|October 17, 2023

概括

亚马逊的自然语言处理评论在未经批准的顺势疗法药物中确定了不良事件 (AE),补充了FDA的报告. 消费者评级预测AE,帮助安全监测.

科学领域:

药物监督药物监督药物监督
自然语言处理自然语言处理.
计算语言学计算语言学

背景情况:

未经批准的非处方 (OTC) 顺势疗法药物缺乏强有力的安全监测.
传统的不良事件报告系统可能无法捕获所有安全信号.
消费者生成的数据为药物监督提供了一个新的来源.

研究的目的:

将自然语言处理 (NLP) 应用于亚马逊消费者评论,以识别与未经批准的OTC顺势疗法药物相关的不良事件 (AE).
将NLP衍生的发现与提交给美国食品和药物管理局不良事件报告系统 (FAERS) 的报告进行比较.

主要方法:

从公开可用的亚马逊评论和FAERS中提取数据.
在亚马逊评论上利用了主题建模和情绪分析.
开发了一种机器学习模型,使用审查数据来预测AEs.
分析了顺势疗法咳/感冒产品作为一个常见的类别.

主要成果:

在亚马逊评论和FAERS数据中确定口腔症状和不愉快的味道是常见的副作用.
亚马逊对已报告的副作用的评论的评级明显较低 (P < .0001).
结合主题建模和亚马逊评级的最佳模型实现了AE预测的平均AUC为0.927.

结论:

对亚马逊评论的NLP分析为消费者对顺势疗法OTC产品的看法和副作用提供了洞察力.
亚马逊的评级可以作为AEs存在的潜在指标.
亚马逊评论可以补充传统的数据来源,用于识别未经批准的顺势疗法药物的副作用.

关键词:

没有处方药的药物.药物不良事件是药物不良事件.消费者偏好消费者偏好药物安全药物安全顺势疗法顺势疗法顺势疗法顺势疗法自然语言处理自然语言处理.

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