Wong Zoie Shui-Yee, Akiyama Masanori
Policy Alternatives Research Institute, The University of Tokyo, Japan.
Stud Health Technol Inform. 2013;192:1053.
WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.
世界卫生组织患者安全部门专注于在国际患者安全分类（ICPS）的基础上，提高患者安全分类的连贯性和表现力。文本分类和统计方法已被证明在利用航空业事故文本信息识别安全问题方面取得了成功。以结构化方式理解医疗事故的分类法一直具有挑战性。英国、加拿大、澳大利亚、日本、中国香港等地已建立了患者安全事件独立报告机制。本研究证明了利用事件文本数据构建统计文本分类器以检测特定类型医疗事故的潜力。本海报展示了一个使用来自全球患者安全警报（GPSA）的227份与用药错误相关的咨询中的结构化文本对形似音似（LASA）用药事故进行分类的示例。该分类器是使用逻辑回归模型构建的。ROC曲线和AUC值表明这是一个相当不错的模型。
