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从纸质报告中提取实验室测试信息.

Ming-Wei Ma1, Xian-Shu Gao2, Ze-Yu Zhang3

  • 1Department of Radiation Oncology, Peking University First Hospital, No.7 Xishiku Street, Beijing, 100034, China.

BMC medical informatics and decision making
|November 6, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种轻量级的自然语言处理 (NLP) 管道,以数字化纸质实验室报告. 该系统有效地提取关键信息,使得结构化数据转换为医疗保健应用程序.

关键词:
有条件的随机字段是有条件的随机字段.提取信息 提取信息实验室测试 实验室测试 实验室测试这是光学字符识别系统.基于纸质的医疗报告.

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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.
  • 数据数字化 数据数字化

背景情况:

  • 许多医疗保健报告仍然以纸质形式,阻碍了数据的可访问性.
  • 数字化纸质实验室报告是具有挑战性的,因为不同的布局和数据类型.
  • 需要一种可扩展,轻量级的方法来将纸质报告转换为结构化数据.

研究的目的:

  • 开发一个端到端的NLP管道,从纸质实验室报告中提取信息.
  • 将各种各样的非标准化纸质报告转换为结构化数字格式.
  • 为临床采用创建具有较低计算资源要求的系统.

主要方法:

  • 开发了一个NLP管道,将光学字符识别 (OCR) 和信息提取 (IE) 结合起来.
  • 该OCR模块识别和定位扫描报告中的文本.
  • 该IE模块提取实体,如测试项目的名称,结果,单位和参考范围使用子模块,包括NER与CRF.

主要成果:

  • 对于文本检测和识别,OCR模块实现了0.93的平均准确性.
  • IE模块在提取关键实验室测试信息时显示了整体F1得分为0.86.
  • 管道在单个CPU上平均在0.78秒内处理每个报告.

结论:

  • 成功开发了一种实用,轻量级的管道,用于数字化纸质实验室报告.
  • 该系统有效地处理各种报告布局和数据类型.
  • 在真实世界数据集上的高性能验证了管道在临床环境中的可行性.