Ma Lili, Tsui Fu-Chiang, Hogan William R, Wagner Michael M, Ma Haobo
Center of Biomedical Informatics, University of Pittsburgh, PA, USA.
AMIA Annu Symp Proc. 2003;2003:410-4.
Surveillance of antibiotic resistance and nosocomial infections is one of the most important functions of a hospital infection control program. We employed the association rule method for automatically identifying new, unexpected, and potentially interesting patterns in hospital infection control. We hypothesized that mining for low-support, low-confidence rules would detect unexpected outbreaks caused by a small number of cases. To build a framework, we preprocessed the data and added new templates to eliminate uninteresting patterns. We applied our method to the culture data collected over 3 months from 10 hospitals in the UPMC Health System. We found that the new process and system are efficient and effective in identifying new, unexpected, and potentially interesting patterns in surveillance data. The clinical relevance and utility of this process await the results of prospective studies.
抗生素耐药性监测和医院感染监测是医院感染控制项目最重要的功能之一。我们采用关联规则方法自动识别医院感染控制中新的、意外的和潜在有趣的模式。我们假设挖掘低支持度、低置信度规则将检测到由少数病例引起的意外暴发。为构建一个框架，我们对数据进行预处理并添加新模板以消除无趣的模式。我们将我们的方法应用于从UPMC医疗系统的10家医院收集的3个月期间的培养数据。我们发现新的流程和系统在识别监测数据中新的、意外的和潜在有趣的模式方面是高效且有效的。这一流程的临床相关性和实用性有待前瞻性研究的结果。
