一个大量的注释事件数据集报告了药物错误.
Zoie S Y Wong1,2, Neil Waters3, Jiaxing Liu4
1Graduate School of Public Health, St. Luke's International University, 3-6-2 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. zoiesywong@gmail.com.
一个新的数据集58,658药物错误报告,机器注释为命名实体识别和意图分析,有助于自动学习患者安全事件.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 患者安全 患者安全
背景情况:
- 药物错误对患者安全至关重要,但事件报告中的非结构化文本阻碍了分析.
- 自动化分析需要大量的注释数据集,用于自然语言处理 (NLP) 模型开发.
研究的目的:
- 为了呈现一个大型的,机器注释的药物错误事件报告集.
- 促进NLP模型的开发,用于自动信息提取和事件学习.
主要方法:
- 为58,658份药物错误报告开发了一种机器注释过程.
- 进行了命名实体识别 (NER) 和意图/事实分析的交叉验证.
- 详细的注释工作流程和技术验证程序.
主要成果:
- 获得了NER的F1分数为0.97,意图/事实分析的F1分数为0.76.
- 有注释的数据集包括478175个命名实体.
- 成功地通过识别预期与实际事件来区分事件类型.
结论:
- 本集体提供了先进的信息提取,用于药物错误分析.
- 数据集和机器注释器支持患者安全学习的未来发展.
- 药物错误报告的自动分析可以通过此资源显著改进.
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