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相关概念视频

Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Methods of Documentation I: Source-Oriented Records01:18

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Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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相关实验视频

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不结构化的电子健康记录的OpenDeID管道 基于规则和变压器的文字笔记:去识别算法开发和验证研究

Jiaxing Liu1, Shalini Gupta2, Aipeng Chen3

  • 1School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.

Journal of medical Internet research
|December 6, 2023
PubMed
概括

本研究介绍了OpenDeID,这是一个混合系统,结合了规则和变压器,以消除电子健康记录中的敏感健康信息的识别. 该系统实现了高精度,证明了其对研究数据隐私的有效性.

关键词:
贝尔特 (BERT) 公司来自变压器的双向编码器表示匿名化 匿名化 匿名化取消身份识别 取消身份识别电子健康记录是电子健康记录.擦洗 擦洗 擦洗 擦洗 擦洗代孕代孕是代孕的一代.没有结构的电子健康记录.

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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 隐私 数据 隐私 数据

背景情况:

  • 非结构化的电子健康记录 (EHR) 是临床和生物医学研究的丰富数据来源.
  • 患者隐私需要在研究使用之前从电子健康记录中删除敏感健康信息 (SHI).
  • 虽然存在基于规则和机器学习的方法来消除身份,但很少有研究将它们与变压器模型相结合.

研究的目的:

  • 使用规则和变压器,为澳大利亚的EHR文本注释开发一条混合去识别管道.
  • 调查预训练词嵌入和基于变压器的语言模型对非识别准确性的影响.

主要方法:

  • 开发了OpenDeID管道,一种混合方法,集成关联规则,监督深度学习和预训练的语言模型.
  • 利用了澳大利亚多中心EHR体的OpenDeID体,包括2,100个病理学报告和38,414个SHI实体.
  • 微调了排放总结BioBERT模型,并纳入了加工前/加工后规则.

主要成果:

  • 在OpenDeID管道中使用微调的BioBERT模型实现了0.9659的最佳F1得分.
  • 开放DeID管道已经在一个大型高等教学医院成功部署.
  • 实时处理了超过8000个非结构化的EHR文本笔记.

结论:

  • 开放DeID管道提供了一种有效的混合方法,用于在非结构化的EHR文本注释中消除敏感健康信息的识别.
  • 管道的性能已经在一个大型的多中心集体上得到验证.
  • 未来的工作包括外部验证,以进一步评估管道的有效性.