Long William
CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
AMIA Annu Symp Proc. 2005;2005:470-4.
We have developed a program for extracting the diagnoses and procedures from the past medical history and discharge diagnoses in the discharge summary of a case and coding these using SNOMED-CT in the UMLS. The program uses a limited amount of natural language processing. Rather, it makes use of the relatively standard structure of the discharge summary, a small dictionary to divide the text into phrases, and the extensive collection of phrases for concepts in the UMLS to do the coding. With this approach the program finds 240 of 250 desired concepts with 19 false positives in 23 discharge summaries.
我们开发了一个程序，用于从病例出院小结中的既往病史和出院诊断中提取诊断和操作信息，并使用统一医学语言系统（UMLS）中的医学系统命名法（SNOMED-CT）对这些信息进行编码。该程序使用了有限的自然语言处理技术。相反，它利用出院小结相对标准的结构、一个小词典将文本分成短语，以及UMLS中概念的大量短语集合来进行编码。通过这种方法，该程序在23份出院小结中找到了250个所需概念中的240个，有19个误报。
