Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.
Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.
复制标题
DOI：
10.1016/j.cmpb.2016.02.007
复制
发表时间：
2016-05
影响因子：
6.1
通讯作者：
Tao C
中科院分区：
文献类型：
--
作者：
Madkour M;Benhaddou D;Tao C
我们生活在日历和时钟中，但时间也是一种抽象，甚至是一种幻觉。时间感既可以是特定于领域的，也可以是复杂的，而且往往是隐含的，需要大量的领域知识来准确识别和利用。在临床领域，从基础设施和治理实践的最新进展中获得的动力使得能够在每个时刻收集大量数据。电子健康记录（EHR）为从业者和研究人员提供这些数据铺平了道路。然而，时间数据的表示，规范化，提取和推理是非常重要的，以挖掘这样的海量数据，从而构建临床时间轴。这项工作的目的是提供一个概述的问题，构建一个时间轴在临床护理点，并总结了国家的最先进的处理时间信息的临床叙述。本文综述了时间的建模与表示、医学自然语言处理的时间提取方法、时间推理与处理方法等三个重要领域的研究进展。本文重点分析了当前语义网技术与现有方法之间存在的差距，并提出了可能的结合点。本综述的主要发现揭示了时间处理的重要性，不仅在构建时间线和临床决策支持系统中，而且作为EHR数据模型和操作的重要组成部分。在临床叙述中提取时间信息是一项具有挑战性的任务。包含本体和语义网将导致更好地评估注释任务，并与医学NLP技术一起，将有助于解决粒度和共指解析问题。
We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic Health Records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. This review surveys the methods used in three important area: modeling and representing of time, Medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. the main findings of this review is revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.