UNLOCKING DATA FROM MEDICAL RECORDS WITH TEXT PROCESSING

描述（摘自应用摘要）：本研究的长期目标 项目是使用自然语言的方法，以提高 电子病历的功能，这是一个来源， 丰富的临床资料。 然而，数据大多是文本形式， 因此不能用于自动化临床应用，例如决策 支持、研究、质量保证和成果评估。 通过使用 自然语言处理器映射报告中的临床信息 将数据转换为结构化的编码临床数据， 可访问，以便其可以被随后的自动化临床 应用. 我们已经证明，建立一个 有效的文本处理器，准确地将文本报告编入 放射学的专业领域。 在这个项目中，我们打算建立在 我们的成功经验，并将扩展到另一个有限的处理器 这是一个不同于放射学的广泛领域， 研究将处理器推广到全医学领域的可行性。 更具体地说，我们将扩大处理器，使其编码 出院体检部分的临床信息 总结，然后到所有的出院总结，我们将重点放在 编码诊断。 我们工作的重点不仅是 扩展语言处理器，但也将关注可伸缩性， 性能、工作量和可移植性方面的评估。 在 此外，由于出院摘要是如此复杂和全面，我们 将不得不扩展临床的正式代表模型， 信息，并开发新的自然语言处理技术， 新的词汇开发工具。 这项工作将继续进行 在临床操作环境中。

DESCRIPTION (Taken from application abstract): The long-term aim of this project is to use natural language methods in order to enhance the functionality of the electronic medical record, which is a source of abundant clinical data. However, the data is mostly in textual form and therefore unusable for automated clinical applications, such as decision support, research, quality assurance, and outcomes assessment. By using a natural language processor to map the clinical information in the reports into structured codified clinical data, the data will be made readily accessible so that it could be utilized by subsequent automated clinical applications. We have already shown that it is possible to build an effective text processor that accurately codifies textual reports within the specialized domain of radiology. In this project we intend to build upon our successful experience and will extend the processor to another limited domain that is different from radiology and to a broad domain in order to study the feasibility of transferring the processor to all of medicine. More specifically, we will broaden the processor so that it codifies clinical information in the physical examination section of the discharge summary and then to all of the discharge summary, where we will focus on coding diagnoses. The emphasis of our work will not only be concerned with extending the language processor but will also focus on scalability, evaluation of the performance, the effort, and the portability aspects. In addition, because discharge summaries are so complex and comprehensive, we will have to extend the formal representational model of the clinical information and also develop new natural language processing techniques and new vocabulary development tools. This work will continue to be performed within an operational clinical setting.