NLM's Software Application to De-identify Clinical Text Documents

叙述性临床报告包含一套丰富的临床知识，对临床研究可能具有无价的价值。然而，它们通常也包含被认为是受保护的健康信息(PHI)的个人识别符，这与使用限制和隐私风险相关。计算性去身份识别试图删除这种叙述性文本中的所有识别符，以便产生可在研究中使用且限制较少且几乎不会对隐私造成风险的去身份文件。计算性去身份识别使用自然语言处理(NLP)工具和技术来识别文本中与患者相关的个人可识别信息(例如，姓名、地址以及电话和社会保障号码)，并对它们进行编辑。这样，患者的隐私得到了保护，临床知识得到了保存。 在探索了现有的识别工具后，美国国家医学图书馆(NLM)开始开发一种新的软件应用程序，该应用程序能够高精度地识别多种临床报告。软件设计使用了许多确定性和概率模式识别算法和各种计算语言方法，以及个人姓名、地址和组织的大型词典。该应用程序接受纯文本或HL7格式的叙述性报告。当报告被格式化为HL7消息时，应用程序利用嵌入在各个HL7段中的已标记的患者相关信息来查找自由文本叙述中的此类信息。该应用软件包括一个用于可视化和标记的编辑器，称为可视化标记工具(VTT)，我们使用它来生成测试该工具的黄金标准。尽管VTT是专门为包含个人可识别、受保护的健康信息的标识符加标签而设计的，但VTT已向更大的NLP社区公开提供，用于扩展词法标签和文本注释。 我们现在正在研究我们的方法在大量标记的临床文档语料库上的性能。这项研究的初步结果表明，在包含个人身份信息的大范围识别器上，计算识别方法可能达到或更好地达到99.9%的灵敏度和99%的特异度的水平。

Narrative clinical reports contain a rich set of clinical knowledge that could be invaluable for clinical research. However, they usually also contain personal identifiers that are considered protected health information (PHI), which is associated with use restrictions and risks to privacy. Computational de-identification seeks to remove all of the identifiers in such narrative text in order to produce de-identified documents that can be used in research with fewer constraints and with almost no risk to privacy. Computational de-identification uses natural language processing (NLP) tools and techniques to recognize patient-related individually identifiable information (e.g., names, addresses, and telephone and social security numbers) in the text, and redacts them. In this way, patient privacy is protected and clinical knowledge is preserved. After exploring existing de-identification tools, the U.S. National Library of Medicine (NLM) began developing a new software application that is capable of de-identifying many kinds of clinical reports with high accuracy. The software design uses a number of deterministic and probabilistic pattern recognition algorithms and various computational linguistic methods as well as large dictionaries of personal names, addresses, and organizations. The application accepts narrative reports in plain text or in HL7 format. When the reports are formatted as HL7 message, the application leverages the labeled patient-related information embedded in various HL7 segments to find such information in the free text narrative. The application software includes an editor for visualization and markup called the Visual Tagging Tool (VTT) that we use to produce gold standards against which to test the tool. Although designed specifically for tagging identifiers that contain personally identifiable, protected health information, VTT has been made publicly available to the greater NLP community for expanded lexical tagging and text annotation. We are now studying the performance of our approach on a large corpus of tagged clinical documents. The preliminary results of this study suggest that computational de-identification methods may attain an accuracy at or better than the level of 99.9% sensitivity and 99% specificity across a large spectrum of identifiers containing personally identifiable information.