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编码后的主诉——对自由文本投诉的自动分析。

Coded Chief Complaints--automated analysis of free-text complaints.

作者信息

Thompson David A, Eitel David, Fernandes Christopher M B, Pines Jesse M, Amsterdam James, Davidson Steven J

机构信息

Department of Emergency Medicine, MacNeal Hospital, Berwyn, IL 60402, USA.

出版信息

Acad Emerg Med. 2006 Jul;13(7):774-82. doi: 10.1197/j.aem.2006.02.013. Epub 2006 May 24.

DOI:10.1197/j.aem.2006.02.013
PMID:16723726
Abstract

OBJECTIVES

To describe a new chief-complaint categorization schema, the development of a computer text-parsing algorithm to automatically classify free-text chief complaints into this schema, and use of these coded chief complaints to describe the case mix of a community emergency department (ED).

METHODS

Coded Chief Complaints for Emergency Department Systems (CCC-EDS) is a new and untested schema of 228 chief complaints, grouped within dimensions of type and system. A computerized text-parsing algorithm for automatically reading and classifying free-text chief complaints into 1 of these 228 coded chief complaints was developed by using a consecutive derivation sample of 46,602 patients who presented to a community teaching-hospital ED in 2004. Descriptive statistics included frequency of patients presenting with the 228 coded chief complaints; percentage of free-text complaints not categorizable by the CCC-EDS; and admission rate, age, and gender differences by chief complaint.

RESULTS

In the derivation sample, the text-parsing algorithm classified 87.5% of 45,329 ED visits with non-null free-text chief complaints into 1 of 194 coded chief complaints. The text-parsing algorithm successfully classified 87.3% of the free-text chief complaints in a validation sample. The five most common coded chief complaints were Abdominal Pain (3,734 visits), Fever (2,234), Chest Pain (2,183), Breathing Difficulty (2,030), and Cuts-Lacerations (2,028).

CONCLUSIONS

The CCC-EDS is a new comprehensive, granular, and useful classification schema for categorizing chief complaints in an ED. A CCC-EDS text-parsing algorithm successfully classified the majority of free-text chief complaints from an ED computer log. These coded chief complaints were used to describe the case mix of a community teaching-hospital ED.

摘要

目的

描述一种新的主诉分类方案,开发一种计算机文本解析算法以自动将自由文本形式的主诉分类到该方案中,并使用这些编码后的主诉来描述社区急诊科的病例组合情况。

方法

急诊科系统编码主诉(CCC-EDS)是一种新的未经测试的方案,包含228种主诉,按类型和系统维度进行分组。通过使用2004年就诊于社区教学医院急诊科的46602例患者的连续推导样本,开发了一种计算机化文本解析算法,用于自动将自由文本形式的主诉读取并分类到这228种编码主诉中的一种。描述性统计包括出现这228种编码主诉的患者频率;CCC-EDS无法分类的自由文本主诉的百分比;以及按主诉划分的住院率、年龄和性别差异。

结果

在推导样本中,文本解析算法将45329次急诊科就诊(自由文本主诉不为空)中的87.5%分类到194种编码主诉中的一种。在验证样本中,文本解析算法成功分类了87.3%的自由文本主诉。五种最常见的编码主诉是腹痛(3734次就诊)、发热(2234次)、胸痛(2183次)、呼吸困难(2030次)和割伤-撕裂伤(2028次)。

结论

CCC-EDS是一种用于急诊科主诉分类的新的全面、细致且有用的分类方案。一种CCC-EDS文本解析算法成功地对急诊科计算机日志中的大多数自由文本主诉进行了分类。这些编码后的主诉被用于描述社区教学医院急诊科的病例组合情况。

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