Raja Uzma, Mitchell Tara, Day Timothy, Hardin J Michael
University of Alabama, USA.
J Healthc Inf Manag. 2008 Summer;22(3):52-6.
Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.
医疗保健信息系统收集了大量有关患者、就诊、处方、医生记录等的文本和数字信息。电子临床记录中包含的信息可提高医疗质量,促进临床和研究工作,减少医疗差错并降低成本。然而,构成健康记录的文档在复杂性、长度和技术词汇使用方面各不相同。这使得知识发现变得复杂。商业文本挖掘工具为从文本数据档案中提取关键信息提供了独特的机会。在本文中,我们分享了一个合作研究项目的经验,该项目通过文本挖掘电子临床记录来开发预测模型。我们概述了文本挖掘过程、现有研究的例子、我们合作项目的经验以及未来的机会。