Campillo-Gimenez Boris, Garcelon Nicolas, Jarno Pascal, Chapplain Jean Marc, Cuggia Marc
INSERM U936, Laboratory of medical informatics, University of Rennes 1, France.
Stud Health Technol Inform. 2013;192:572-5.
The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.
手术部位感染（SSI）监测有助于法国医院的风险管控。人工识别感染成本高、耗时久，且限制了专业团队对预防措施的推广。采用自动检测策略的替代方法有望改善这种监测。本研究描述了一种基于临床数据仓库中存储的医疗报告文本分析的神经外科手术部位感染自动检测策略。该方法首先使用NOMINDEX从全文报告中进行富集和概念提取，其次使用向量空间模型进行文本相似度测量。将文本检测结果与基于自我申报的传统策略以及使用诊断相关组数据库的自动检测结果进行了比较。文本挖掘方法显示出最佳的检测准确性，召回率和精确率分别为92%和40%，并证实了重新利用全文医疗报告进行手术部位感染自动检测的价值。
