非标准化药物数据的自动识别:使用RxNorm在GEMINI多中心医院数据上的RxNorm进行可扩展和灵活的数据标准化管道
Riley Waters1, Sarah Malecki2, Sharan Lail1
1St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
JAMIA open
|August 11, 2023
概括
吉米尼-RxNorm系统标准化来自多个来源的药物数据,实现高准确性和效率. 这种自动化过程大大减少了研究人员手动审查的时间.
科学领域:
- 医疗信息学 医疗信息学
- 药理学 药理学是指药理学的学科.
- 数据科学数据科学数据科学
背景情况:
- 患者数据库从各种来源汇总药物数据,需要标准化才能进行可靠的分析.
- 标准化药物数据对于在各种药物类别和细节上准确的研究查询至关重要.
研究的目的:
- 实施和评估用于标准化药房数据的GEMINI-RxNorm系统.
- 评估GEMINI-RxNorm在处理各种药物数据输入中的准确性,效率和可扩展性.
主要方法:
- GEMINI-RxNorm自动化了RxNorm工具和其他数据集的集成,以从药房订单中识别药物概念.
- 该系统处理了来自多次住院的200多万份药房订单,将自由文本字段与RxNorm概念标识符匹配起来.
- 用户界面可方便预测匹配的手动验证,以确保高精度.
主要成果:
- GEMINI-RxNorm成功处理了99.6%的药房订单,在13种药物类别中,召回超过0.985.
- F1分数和精度保持在0.90以上,使有效的手动审查能够达到100%的精度.
- 该系统大大减少了每个订单的审查时间 (从30秒减少到5秒),并且需要审查的行数减少了高达99.99%.
结论:
- 对于药房数据标准化,GEMINI-RxNorm提供了一个准确,高效,灵活和可扩展的解决方案.
- 该系统的高效手动验证过程使研究人员能够在药物数据标准化中实现近乎完美的准确性.
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