Malmasi Shervin, Sandor Nicolae L, Hosomura Naoshi, Goldberg Matt, Skentzos Stephen, Turchin Alexander
Alexander Turchin, MD, MS, Brigham and Women's Hospital, Boston, MA, Email:
Appl Clin Inform. 2017 May 3;8(2):447-453. doi: 10.4338/ACI-2017-01-IE-0018.
Information Extraction methods can help discover critical knowledge buried in the vast repositories of unstructured clinical data. However, these methods are underutilized in clinical research, potentially due to the absence of free software geared towards clinicians with little technical expertise. The skills required for developing/using such software constitute a major barrier for medical researchers wishing to employ these methods. To address this, we have developed Canary, a free and open-source solution designed for users without natural language processing (NLP) or software engineering experience. It was designed to be fast and work out of the box via a user-friendly graphical interface.
信息提取方法有助于发现隐藏在大量非结构化临床数据存储库中的关键知识。然而,这些方法在临床研究中的应用尚未得到充分利用,这可能是由于缺乏面向技术专业知识较少的临床医生的免费软件。开发/使用此类软件所需的技能对希望采用这些方法的医学研究人员构成了重大障碍。为了解决这个问题,我们开发了Canary,这是一个免费的开源解决方案,专为没有自然语言处理(NLP)或软件工程经验的用户设计。它的设计目标是快速运行,并通过用户友好的图形界面即开即用。