Automating the Identification of Patient Safety Incident Reports Using Multi-Label Classification

摘要：

　　Automated identification provides an efficient way to categorize patient safety incidents. Previous studies have focused on identifying single incident types relating to a specific patient safety problem, e.g., clinical handover. In reality, there are multiple types of incidents reflecting the breadth of patient safety problems and a single report may describe multiple problems, i.e., it can be assigned multiple type labels. This study evaluated the abilty of multi-label classification methods to identify multiple incident types in single reports. Three multi-label methods were evaluated: binary relevance, classifier chains and ensemble of classifier chains. We found that an ensemble of classifier chains was the most effective method using binary Support Vector Machines with radial basis function kernel and bag-of-words feature extraction, performing equally well on balanced and stratified datasets, (F-score: 73.7% vs. 74.7%). Classifiers were able to identify six common incident types: falls, medications, pressure injury, aggression, documentation problems and others.

关键词： Machine Learning Risk Management Patient Safety

作者: Ying Wang Enrico Coiera William Runciman Farah Magrabi

作者单位: Centre for Health Informatics,Australian Institute of Health Innovation,Macquarie University,Austral Centre for Population Health Research,School of Health Sciences,University of South Australia,Austra

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 609-613

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）