Suppr超能文献

基于电子病历的哮喘急性加重发作自动识别用于质量评估

Automated identification of episodes of asthma exacerbation for quality measurement in a computer-based medical record.

作者信息

Aronow D B, Cooley J R, Soderland S

机构信息

Center for Intelligent Information Retrieval, University of Massachusetts, Amherst, USA.

出版信息

Proc Annu Symp Comput Appl Med Care. 1995:309-13.

Abstract

Harvard Community Health Plan and the Center for Intelligent Information Retrieval are developing tools to support automated quality fo care measurement from clinical text data. A statistically based text classification system uses semantic features in computerized encounter notes to identify acute exacerbations of asthma. Individual encounter notes are sorted in bins of highly likely, highly unlikely and uncertain likelihood of documenting exacerbation, and then aggregated into episodes of exacerbation for frequency analysis. It is estimated that this approach could reduce the burden of manual chart review by 65%.

摘要

哈佛社区健康计划和智能信息检索中心正在开发工具,以支持从临床文本数据中自动进行医疗质量测量。一个基于统计的文本分类系统利用计算机化会诊记录中的语义特征来识别哮喘急性加重情况。将各个会诊记录按照记录加重情况的可能性高、可能性低和不确定进行分类,然后汇总成加重发作事件进行频率分析。据估计,这种方法可将人工病历审查的负担减轻65%。

相似文献

引用本文的文献

本文引用的文献

1
Automated ambulatory medical records systems. An orphan technology.
Int J Technol Assess Health Care. 1992 Fall;8(4):598-609. doi: 10.1017/s0266462300002300.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验