Department of Biomedical Informatics, Columbia University, NY, USA; Department of Systems Biology, Columbia University, NY, USA; Department of Medicine, Columbia University, NY, USA; Observational Health Data Sciences and Informations (OHDSI), NY, USA; Department of Software and Information Systems Engineering, Ben Gurion Univeristy, Beer Sheva, Israel.
Department of Biomedical Informatics, Columbia University, NY, USA; Department of Systems Biology, Columbia University, NY, USA; Department of Medicine, Columbia University, NY, USA; Observational Health Data Sciences and Informations (OHDSI), NY, USA.
J Biomed Inform. 2017 Nov;75:70-82. doi: 10.1016/j.jbi.2017.07.018. Epub 2017 Aug 17.
Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.
预测医疗事件，如临床操作，对于预防疾病、了解疾病机制和提高患者护理质量至关重要。尽管电子健康记录中的纵向临床数据提供了开发预测模型的机会，但这些数据的使用面临着重大挑战。主要是，虽然数据是纵向的，代表了数千个具有持续时间的概念事件，但它们也很稀疏，这使得传统分析方法的应用变得复杂。此外，这里提出的框架利用了事件的持续时间和间隔。电子医疗保健数据的国际标准使用时代或时间间隔来表示诸如操作、状况和药物暴露等数据元素。这些时代既包含事件又包含持续时间，并能够应用时间间隔挖掘-这是数据挖掘的一个相对较新的子领域。在这项研究中，我们提出了 Maitreya，这是一个用于纵向临床数据的时间间隔分析框架。Maitreya 发现了与时间相关的频繁时间间隔模式（TIRP），我们将其用作建模临床事件的预后标志物。我们引入了三个新的 TIRP 度量标准，它们是水平支持的归一化版本，代表每个患者的 TIRP 实例数。我们使用三种新的 TIRP 表示度量标准来评估 Maitreya，与没有时间表示和以前的 TIRP 度量标准相比，比较了 28 种常见且具有临床意义的操作。我们还评估了使 Allen 关系更灵活的 epsilon 值，与默认的零相比，使用了 30、60、90 和 180 天的几个设置。对于其中的二十二个操作，使用时间模式作为预测因子比非时间特征更有效，并且使用垂直归一化的水平支持度量标准作为特征来表示 TIRP 是最有效的。使用三十天的 epsilon 值略优于零。
