使用FASTVISU在两小时内查看741份患者记录。
Reviewing 741 patients records in two hours with FASTVISU.
作者信息
Escudié Jean-Baptiste, Jannot Anne-Sophie, Zapletal Eric, Cohen Sarah, Malamut Georgia, Burgun Anita, Rance Bastien
机构信息
University Hospital Georges Pompidou (HEGP); AP-HP, Paris, France; INSERM; UMRS1138, Paris Descartes University, Paris, France.
University Hospital Georges Pompidou (HEGP); AP-HP, Paris, France.
出版信息
AMIA Annu Symp Proc. 2015 Nov 5;2015:553-9. eCollection 2015.
The secondary use of electronic health records opens up new perspectives. They provide researchers with structured data and unstructured data, including free text reports. Many applications been developed to leverage knowledge from free-text reports, but manual review of documents is still a complex process. We developed FASTVISU a web-based application to assist clinicians in reviewing documents. We used FASTVISU to review a set of 6340 documents from 741 patients suffering from the celiac disease. A first automated selection pruned the original set to 847 documents from 276 patients' records. The records were reviewed by two trained physicians to identify the presence of 15 auto-immune diseases. It took respectively two hours and two hours and a half to evaluate the entire corpus. Inter-annotator agreement was high (Cohen's kappa at 0.89). FASTVISU is a user-friendly modular solution to validate entities extracted by NLP methods from free-text documents stored in clinical data warehouses.
电子健康记录的二次利用开辟了新的前景。它们为研究人员提供结构化数据和非结构化数据，包括自由文本报告。已经开发了许多应用程序来利用自由文本报告中的知识，但文档的人工审查仍然是一个复杂的过程。我们开发了FASTVISU，这是一个基于网络的应用程序，用于协助临床医生审查文档。我们使用FASTVISU审查了一组来自741名乳糜泻患者的6340份文档。首次自动筛选将原始文档集精简至来自276名患者记录的847份文档。两名经过培训的医生对这些记录进行审查，以确定15种自身免疫性疾病的存在情况。评估整个文档集分别花费了两小时和两个半小时。注释者间一致性很高（科恩kappa系数为0.89）。FASTVISU是一个用户友好的模块化解决方案，用于验证通过自然语言处理方法从存储在临床数据仓库中的自由文本文档中提取的实体。
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