Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen’s time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.

在电子患者数据中搜索患者队列通常需要定义选择标准之间的时间约束。但是，除了一定程度的时间复杂性外，在对此类约束进行建模时，当前翻译研究平台中实现的基于图形的非图形方法可能会受到限制。我们认为，需要一种易于访问且可实现的完全图形化的方法来创建时间查询。我们旨在以新的图形符号来应对这一挑战。基于艾伦的时间间隔代数，它可以通过排列描述符号时间间隔的简单水平条来对时间查询进行建模。为了使我们的方法适用于复杂的时间模式，我们应用了两个扩展：持续时间间隔，我们可以推断出患者事件之间的相对时间距离，并且使用时间间隔修饰符，我们支持对患者事件进行计数和排除，以及限制数值。我们描述了如何从该符号生成数据库查询。我们提供了一个原型实现，包括一个时间查询建模前端和一个连接到i2b2系统的实验后端。我们在MIMIC-III数据库上评估了我们的建模方法，以证明它可用于对典型的时间表型查询进行建模。