Dipartimento di Informatica, Università di Torino, Italy.
Computer Science Institute, DISIT, Università del Piemonte Orientale, Alessandria, Italy.
Artif Intell Med. 2018 Mar;86:33-52. doi: 10.1016/j.artmed.2018.01.004. Epub 2018 Feb 21.
Temporal information plays a crucial role in medicine. Patients' clinical records are intrinsically temporal. Thus, in Medical Informatics there is an increasing need to store, support and query temporal data (particularly in relational databases), in order, for instance, to supplement decision-support systems. In this paper, we show that current approaches to relational data have remarkable limitations in the treatment of "now-relative" data (i.e., data holding true at the current time). This can severely compromise their applicability in general, and specifically in the medical context, where "now-relative" data are essential to assess the current status of the patients. We propose a theoretically grounded and application-independent relational approach to cope with now-relative data (which can be paired, e.g., with different decision support systems) overcoming such limitations. We propose a new temporal relational representation, which is the first relational model coping with the temporal indeterminacy intrinsic in now-relative data. We also propose new temporal algebraic operators to query them, supporting the distinction between possible and necessary time, and Allen's temporal relations between data. We exemplify the impact of our approach, and study the theoretical and computational properties of the new representation and algebra.
时间信息在医学中起着至关重要的作用。患者的临床记录本质上是具有时间属性的。因此，在医学信息学中，越来越需要存储、支持和查询时间数据（特别是在关系型数据库中），例如，为了补充决策支持系统。在本文中，我们表明，当前的关系型数据方法在处理“当前相关”数据（即在当前时间有效的数据）方面存在显著的局限性。这可能严重影响其在一般情况下的适用性，特别是在医学领域，其中“当前相关”数据对于评估患者的当前状况至关重要。我们提出了一种基于理论且与应用无关的关系型方法来处理当前相关数据（例如，可以与不同的决策支持系统结合使用），从而克服了这些局限性。我们提出了一种新的时间关系型表示方法，这是第一个能够处理当前相关数据中内在的时间不确定性的关系型模型。我们还提出了新的时间代数运算符来查询这些数据，支持区分可能时间和必要时间，以及 Allen 的数据之间的时间关系。我们举例说明了我们方法的影响，并研究了新表示和代数的理论和计算性质。
