TY - JOUR AU - Peterson, Kelly S AU - Lewis, Julia AU - Patterson, Olga V AU - Chapman, Alec B AU - Denhalter, Daniel W AU - Lye, Patricia A AU - Stevens, Vanessa W AU - Gamage, Shantini D AU - Roselle, Gary A AU - Wallace, Katherine S AU - Jones, Makoto PY - 2021 DA - 2021/3/24 TI -基于临床记录的旅行史自动提取为突发传染病事件的检测提供信息;算法开发和验证JO - JMIR公共卫生监测SP - e26719 VL - 7 IS - 3kw -自然语言处理KW -机器学习KW -旅行史KW - COVID-19 KW -寨卡病毒KW -传染病监测KW -监测应用KW -生物监测KW -电子健康记录AB -背景:患者旅行史对于评估不断发展的传染病事件至关重要。在电子健康记录中获取此类信息可能具有挑战性，因为通常只能以非结构化文本提供。目的:本研究旨在评估在退伍军人事务部不同医疗机构和数百万患者的非结构化临床文件中注释和自动提取旅行历史提及的可行性。关于旅行暴露的信息增强了现有的监测应用，以加强快速应对公共卫生威胁的准备工作。方法:采用半自动自举法对虫媒病毒病相关临床文献进行标注。使用带注释的实例作为训练数据，开发模型以从非结构化临床文本中提取任何提及美国大陆以外的确认旅行地点的内容。评估了自动文本处理模型，包括机器学习和神经语言模型的提取准确性。结果:在4584个注释实例中，2659个(58%)包含肯定提及的旅行史，347个(7.6%)包含否定提及的旅行史。注释器间的一致导致文档级的Cohen kappa为0.776。 Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2021/3/e26719 UR - https://doi.org/10.2196/26719 UR - http://www.ncbi.nlm.nih.gov/pubmed/33759790 DO - 10.2196/26719 ID - info:doi/10.2196/26719 ER -