混合方法结合了深度学习和基于规则的专家系统,用于从处方中提取概念
概括
这项研究引入了一种新的方法,用于从处方中提取关键信息,提高医疗保健应用的准确性. 该方法将基于规则的系统与先进的深度学习相结合,以更好地识别概念.
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.
背景情况:
- 从处方中提取概念对于医疗保健应用,如药物监测和药物遵守至关重要.
- 基于规则的系统与处方中的自然语言指示的复杂性作斗争.
研究的目的:
- 开发和评估一种混合方法,从处方文本中准确地提取概念.
- 改进现有的方法来识别关键信息,如剂量,频率和持续时间.
主要方法:
- 使用了基于规则的专家系统和深度学习 (DL) 模型的组合.
- 一个微调的BERT变压器和基于Gram Convolutional神经网络 (CNN) 的命名实体识别 (NER) 架构构成了DL模块.
- 域启发学,智能标签和引导被用来提高DL模型的性能.
主要成果:
- 混合方法在从现实世界处方数据中提取概念时获得了高的评估分数.
- 这种方法与文献中现有的方法相比,显示出更高的性能.
- 成功地从复杂的处方指示中提取了包括频率,剂量和持续时间在内的概念.
结论:
- 提出的有针对性的方法在从医疗处方中提取概念方面取得了重大进展.
- 这种方法为各种下游医疗保健决策过程提供了坚实的基础.
- 在从医生的处方中提取概念方面取得了最先进的性能.
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