Interactive Search and Review of Clinical Records with Multi-layered Semantic Ann

描述(由申请人提供)： 将科学转化为实践的一个关键因素是为临床研究寻找患者群体的能力。许多研究依靠管理数据来选择相关患者进行比较有效性研究，但管理数据的局限性是众所周知的。许多对临床研究至关重要的信息都锁定在自由文本的口述报告中，例如病史、体检和放射学报告。数据仓库，如匹兹堡大学的医疗档案检索系统(MARS)，对于通过索引词搜索识别用于临床研究研究的患者超集很有用。然而，简单的基于文本的查询在有效性上也是有限的，研究人员经常需要阅读数百或数千份报告来过滤假阳性案例。目前的流程既耗时又昂贵。它们导致了可验证假说的开发和与广大医学界分享研究结果的能力之间的长期拖延。 这个问题的一个潜在解决方案是预先注释未识别的临床报告，以促进更智能和更复杂的检索和审查。临床报告具有丰富的含义和结构，可以使用自然语言处理技术在许多不同的层次上进行注释。然而，目前还不清楚什么类型的注释对临床研究人员最有帮助，也不清楚如何显示注释以最好地辅助手动审查报告。NLP系统使用的注释模式与帮助研究人员检索数据以进行回溯性研究的用户界面之间存在相互依赖关系。在这份提案中，我们将交互式地修订NLP注释模式，并根据用户对特定研究的患者数据进行审查的反馈，探索各种注释显示方法。 我们假设，依赖于NLP注释的临床文本的交互式搜索应用程序将提高为临床研究研究寻找患者的准确性和效率，并将支持可视化技术来查看数据，从而提高研究人员审查患者数据的能力。

DESCRIPTION (provided by applicant): A critical element of translating science into practice is the ability to find patient populations for clinical research. Many studies rely on administrative data for selecting relevant patients for studies of comparative effectiveness, but the limitations of administrative data is well-known. Much of the information critical for clinical research is locked in free-text dictated reports, such as history and physical exams and radiology reports. Data repositories, such as the Medical Archival Retrieval System (MARS) at the University of Pittsburgh, are useful for identifying supersets of patients for clinical research studies through indexed word searches. However, simple text-based queries are also limited in their effectiveness, and researchers are often left reading through hundreds or thousands of reports to filter out false positive cases. Current processes are time-consuming and extraordinarily expensive. They lead to long delays between the development of a testable hypothesis and the ability to share findings with the medical community at large. A potential solution to this problem is pre-annotating de-identified clinical reports to facilitate more intelligent and sophisticated retrieval and review. Clinical reports are rich in meaning and structure and can be annotated at many different levels using natural language processing technology. It is not clear, however, what types of annotations would be most helpful to a clinical researcher, nor is it clear how to display the annotations to best assist manual review of reports. There is interdependence between the annotation schema used by an NLP system and the user interface for assisting researchers in retrieving data for retrospective studies. In this proposal, we will interactively revise an NLP annotation schema as well as explore various methods for annotation display based on feedback from users reviewing patient data for specific research studies. We hypothesize that an interactive search application that relies on NLP-annotated clinical text will increase the accuracy and efficiency of finding patients for clinical research studies and will support visualization techniques for viewing the data in a way that improves a researcher's ability to review patient data.