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

使用多层语义安娜对临床记录进行交互式搜索和审查

• 批准号: 8333306
• 负责人: WENDY W. CHAPMAN
• 金额: $ 59.24万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2015-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8333306
• 关键词: Automated Annotation; Clinical; Clinical Research; Communities; Data; Databases; Development; Effectiveness; Elements; Feedback; Imagery; Lead; Left; Manuals; Medical; Methods; Natural Language Processing; Outcome; Patients; Process; Property; Radiology Specialty; Reading; Recording of previous events; Records; Reporting; Research; Research Personnel; Retrieval; Retrospective Studies; Science; Semantics; Solutions; Structure; System; Techniques; Technology; Text; Time; Translating; Translational Research; Universities; base; comparative effectiveness; computer human interaction; improved; indexing; novel; patient population; research study; success

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.

描述（由申请人提供）： 将科学转化为实践的关键要素是能够找到临床研究的患者人群。许多研究依靠行政数据来选择相关患者以进行比较效率研究，但是行政数据的局限性是众所周知的。临床研究至关重要的许多信息都锁定在自由文本规定的报告中，例如历史，体格检查和放射学报告。数据存储库，例如匹兹堡大学的医学档案检索系统（MARS），可用于通过索引单词搜索来识别患者的超级临床研究。但是，简单的基于文本的查询的有效性也有限，研究人员通常会通过数百或数千份报告读取误报案例。当前的流程耗时且非常昂贵。它们导致了可检验的假设的发展与与整个医学界分享发现的能力之间的延迟。 解决此问题的一种潜在解决方案是预先通知的去识别临床报告，以促进更聪明，更复杂的检索和审查。临床报告具有丰富的意义和结构，可以使用自然语言处理技术在许多不同的层面上注释。但是，尚不清楚哪种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协助报告报告。 NLP系统使用的注释模式与用户界面之间有相互依赖性，以帮助研究人员检索数据进行回顾性研究。在此提案中，我们将根据用户的反馈来审查患者数据的特定研究研究，以互动修改NLP注释模式，并探索各种注释显示。 我们假设依赖于NLP注销的临床文本的交互式搜索应用程序将提高寻找患者进行临床研究的准确性和效率，并将支持可视化技术以提高研究人员审查患者数据的能力的方式来查看数据。