基于双向长期短期记忆的药物不良反应检测 文章使用韩国社交网络服务 数据:深度学习方法
Chung-Chun Lee1, Seunghee Lee2, Mi-Hwa Song3
1Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.
JMIR medical informatics
|November 20, 2024
概括
这项研究开发了一个深度学习模型,从韩国社交媒体数据中检测药物不良反应 (ADR). 该模型实现了高精度,证明了使用社会数据用于自动化ADR监控的可行性.
科学领域:
- 药物监督 药物监督 药物监督
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 社交网络服务 (SNS) 产生了大量的用户数据,为现代生活提供了洞察力.
- 以前的研究已经利用SNS数据来提取药物信息,突出了药物监督的潜力.
- 药物不良反应 (ADR) 的早期检测对公共卫生至关重要,需要先进的监测系统.
研究的目的:
- 开发一种深度学习模型,使用韩国社交媒体数据对与药物不良反应 (ADRs) 相关的帖子进行分类.
- 研究基于循环神经网络的模型在从用户生成的内容中识别药物诱导的不良事件时的有效性.
- 在自动化药物安全监测中利用社会数据建立一个可行的过程.
主要方法:
- 收集的韩国SNS数据涉及凯托,一个警告药物,和应用自然语言处理 (NLP) 技术.
- 通过使用药物名称和ADR关键词之间的关联分析来过相关帖子,然后进行手动标签以创建培训数据.
- 开发了一种使用word2vec嵌入式的双向长短期记忆 (Bi-LSTM) 分类模型,并与其他机器学习模型对其性能进行了验证.
主要成果:
- 为药物名称,ADRs和停止词生成韩国特定的词典,以提高模型的准确性.
- 开发的Bi-LSTM模型在分类ADR帖子时,获得了凯托的85%准确性和阿塞克洛芬的80%准确性.
- 该研究证实了使用社会数据用于自动检测和监测ADR的可行性.
结论:
- 建议使用SNS数据开发ADR后分类模型的新过程.
- 该模型通过识别已知的ADR模式的帖子来有效地过高质量的数据.
- 这项研究验证了社会数据分析在自动化药物监督方面的潜力.
关键词:
韩国 韩国 韩国 韩国韩国文本数据 韩国文本数据药物不良反应 药物不良反应分类模型的分类模型.深度学习是一种深度学习.药物检测 药物检测 药物检测药物监管监督是指对药物进行监督.医学 医学 医学 医学 医学年长的年长的年长的年长的年龄较大的成年人.社会数据社会数据社交网络服务 社交网络服务社交网络服务 社交网络服务更多相关视频
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