标题： clustra：一种用于分析大型电子健康记录数据中纵向轨迹的多平台k均值聚类算法 显示英文标题

标题： clustra: A multi-platform k-means clustering algorithm for analysis of longitudinal trajectories in large electronic health records data

摘要： 背景与目的：在一段时间内收集的变量，或纵向数据，例如电子健康记录数据中的生物测量值，难以用单一时点进行总结，因此可以更全面地概念化为随时间变化的轨迹。 使用纵向数据进行聚类分析进一步允许对具有相似轨迹的受试者群体进行临床表示，并识别可作为风险因素或疾病结果进行研究的独特特征，或表型。 估计这些聚类轨迹的一些挑战在于处理不一致时间间隔的观测数据，以及算法在不同编程语言中的可用性。 方法：我们提出使用k均值算法结合薄板回归样条进行纵向轨迹聚类，该算法在多个平台上实现，包括R包clustra和相应的\SAS 宏。 \SAS 宏支持灵活的聚类方法，还包含聚类的可视化以及用于诊断评估适当聚类数的轮廓图。 并行设计的R包具有类似功能，同时还具有多核处理和基于Rand指数的诊断功能。 结果：当应用于基于退伍军人事务医疗接受者真实数据的模拟血压测量示例时，该包和宏取得了可比较的结果。 结论：R包clustra和SAS宏集成了适用于大型电子健康记录数据的K均值聚类算法。 这些实现在两个平台上都能取得可比较的结果，满足熟悉或受限于其中一个平台的研究人员的需求。 显示英文摘要

摘要： Background and Objective: Variables collected over time, or longitudinally, such as biologic measurements in electronic health records data, are not simple to summarize with a single time-point, and thus can be more holistically conceptualized as trajectories over time. Cluster analysis with longitudinal data further allows for clinical representation of groups of subjects with similar trajectories and identification of unique characteristics, or phenotypes, that can be investigated as risk factors or disease outcomes. Some of the challenges in estimating these clustered trajectories lie in the handling of observations at inconsistent time intervals and the usability of algorithms across programming languages. Methods: We propose longitudinal trajectory clustering using a k-means algorithm with thin-plate regression splines, implemented across multiple platforms, the R package clustra and corresponding \SAS macros. The \SAS macros accommodate flexible clustering approaches, and also include visualization of the clusters, and silhouette plots for diagnostic evaluation of the appropriate cluster number. The R package, designed in parallel, has similar functionality, with additional multi-core processing and Rand-index-based diagnostics. Results: The package and macros achieve comparable results when applied to an example of simulated blood pressure measurements based on real data from Veterans Affairs Healthcare recipients who were initiated on anti-hypertensive medication. Conclusion: The R package clustra and the SAS macros integrate a K-means clustering algorithm for longitudinal trajectories that operates with large electronic health record data. The implementations provide comparable results in both platforms, satisfying the needs of investigators familiar with, or constrained by access to, one or the other platform.