从出院小结中提取诊断信息。
Extracting diagnoses from discharge summaries.
作者信息
Long William
机构信息
CSAIL, Massachusetts Institute of Technology, Cambridge, MA, USA.
出版信息
AMIA Annu Symp Proc. 2005;2005:470-4.
Abstract
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.
摘要
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。
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本文引用的文献
1
Automated encoding of clinical documents based on natural language processing.基于自然语言处理的临床文档自动编码
J Am Med Inform Assoc. 2004 Sep-Oct;11(5):392-402. doi: 10.1197/jamia.M1552. Epub 2004 Jun 7.
2
Electronically screening discharge summaries for adverse medical events.对出院小结进行电子筛查以查找不良医疗事件。
J Am Med Inform Assoc. 2003 Jul-Aug;10(4):339-50. doi: 10.1197/jamia.M1201. Epub 2003 Mar 28.
3
A simple algorithm for identifying negated findings and diseases in discharge summaries.一种用于识别出院小结中否定性检查结果和疾病的简单算法。
J Biomed Inform. 2001 Oct;34(5):301-10. doi: 10.1006/jbin.2001.1029.
4
5
6
Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS.使用通用否定检测增强医学文档的概念索引：一项使用统一医学语言系统的定量研究
J Am Med Inform Assoc. 2001 Nov-Dec;8(6):598-609. doi: 10.1136/jamia.2001.0080598.
7
Aggregating UMLS semantic types for reducing conceptual complexity.聚合统一医学语言系统语义类型以降低概念复杂性。
Stud Health Technol Inform. 2001;84(Pt 1):216-20.
8
The Unified Medical Language System.统一医学语言系统
Methods Inf Med. 1993 Aug;32(4):281-91. doi: 10.1055/s-0038-1634945.
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