IBM T.J. Watson Research Center, 1101 Kitchawan Road, P.O. Box 218, Yorktown Heights, NY 10598, USA.
J Biomed Inform. 2014 Apr;48:148-59. doi: 10.1016/j.jbi.2014.01.007. Epub 2014 Jan 28.
Patients' medical conditions often evolve in complex and seemingly unpredictable ways. Even within a relatively narrow and well-defined episode of care, variations between patients in both their progression and eventual outcome can be dramatic. Understanding the patterns of events observed within a population that most correlate with differences in outcome is therefore an important task in many types of studies using retrospective electronic health data. In this paper, we present a method for interactive pattern mining and analysis that supports ad hoc visual exploration of patterns mined from retrospective clinical patient data. Our approach combines (1) visual query capabilities to interactively specify episode definitions, (2) pattern mining techniques to help discover important intermediate events within an episode, and (3) interactive visualization techniques that help uncover event patterns that most impact outcome and how those associations change over time. In addition to presenting our methodology, we describe a prototype implementation and present use cases highlighting the types of insights or hypotheses that our approach can help uncover.
患者的病情常常以复杂且看似不可预测的方式发展。即使在相对狭窄且明确的治疗阶段内，患者在病情进展和最终结果方面的差异也可能非常显著。因此，了解与结果差异最相关的人群中观察到的事件模式是使用回顾性电子健康数据进行许多类型研究的重要任务。在本文中，我们提出了一种用于交互式模式挖掘和分析的方法，该方法支持从回顾性临床患者数据中挖掘出的模式的即席可视化探索。我们的方法结合了（1）用于交互式指定阶段定义的可视化查询功能，（2）帮助发现阶段内重要中间事件的模式挖掘技术，以及（3）帮助揭示对结果影响最大的事件模式以及这些关联如何随时间变化的交互可视化技术。除了介绍我们的方法之外，我们还描述了一个原型实现，并介绍了用例，突出了我们的方法可以帮助揭示的见解或假设类型。
