Assessing surgical site infection risk factors using electronic medical records and text mining

被引：24
|
作者：
Michelson, James D. [1 ,2 ]
Pariseau, Jenna S. [1 ,2 ]
Paganelli, William C. [3 ]
机构：
[1] Dept Orthoped, Burlington, VT USA
[2] Dept Rehabil, Burlington, VT USA
[3] Univ Vermont, Coll Med, Dept Anesthesia, Burlington, VT 05401 USA
来源：
AMERICAN JOURNAL OF INFECTION CONTROL | 2014年 / 42卷 / 03期
关键词：
Surgery; Surveillance; TOTAL HIP; ADMINISTRATIVE DATA; WOUND-INFECTION; NECROSIS-FACTOR; FOLLOW-UP; RHEUMATOID-ARTHRITIS; COMORBIDITY INDEX; SPINE SURGERY; SURVEILLANCE; COMPLICATIONS;
D O I：
10.1016/j.ajic.2013.09.007
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
摘要：
Text mining techniques to detect surgical site infections (SSI) in unstructured clinical notes were used to improve SSI detection. In conjuction with data from an integrated electronic medical record, all of the 22 SSIs detected by traditional hospital-based surveillance were found using text mining, along with an additional 37 SSIs not detected by traditional surveillance. Copyright (C) 2014 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
引用
收藏
页码：333 / 336
页数：4
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