在社交媒体中检测药物不良事件:简要文献综述
Imane Guellil1, Yousra Berrachedi2, Nidhal Eddine Chenni2
1Edinburgh University, Edinburgh, UK.
概括
本综述分析了使用自然语言处理 (NLP) 来检测来自社交媒体的不良药物事件 (ADEs) 的100项研究. 它强调了数据噪声和多语言内容等挑战,以及变压器模型的主导地位.
科学领域:
- 药物监测和公共卫生
- 计算语言学和人工智能
- 数字健康和社交媒体分析
背景情况:
- 药物不良事件 (ADEs) 给公共卫生带来了重大挑战,需要创新的监测方法.
- 社交媒体提供了丰富的,实时的患者生成的药物监督数据来源.
- 传统的药监方法可以通过分析在线话语来补充.
研究的目的:
- 系统地审查最近的自然语言处理 (NLP) 研究,重点是识别社交媒体文本中的ADEs.
- 综合了2017年至2025年间发表的100项同行评审研究的研究结果.
- 识别基于社交媒体的ADE监测NLP的趋势,反复出现的挑战和未来方向.
主要方法:
- 通过谷歌学者识别的100项同行评审研究的系统文献审查.
- 不包括专利,协议和缺乏方法细节的会议摘要.
- 将研究分类为分类,提取,规范化,语料库创建和分析任务,对目标,数据源,预处理,模型和评估指标进行数据提取.
主要成果:
- 推特是主要的数据来源 (78%),英语是主要语言 (约. 95%) 的情况.
- 基于变压器的模型,特别是BERT变体 (例如BERTweet,BioBERT) 在超过60%的研究中使用.
- 主要挑战包括噪音/不平衡的数据,多语言/代码混合的内容,隐含的药物事件表达式,以及缺乏全面的注释准则 (12%的论文).
结论:
- 虽然NLP技术在社交媒体上对ADE监控具有前景,但数据质量,多语言支持和模型部署的挑战仍然存在.
- 变压器模型已经成为标准,但诸如阶级不平衡和隐含的ADE提及等问题需要进一步关注.
- 未来的研究应该集中在模型可解释性,低资源语言,多式联运数据和与监管机构更紧密的合作,以便在现实世界中应用.
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