Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse.

中文翻译：

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EHRtemporalVariability：描绘电子健康记录中的时间数据集变化。

医疗保健过程或协议的时间可变性是医学所固有的。这种可变性可能会导致数据集变化，这是将电子健康记录 (EHR) 重用于次要目的时的数据质量问题。时间数据集的变化可以表现为趋势，也可以表现为数据统计分布随时间的突然或季节性变化。后者在多模式和高度编码的数据中特别复杂。这些变化如果不加以描述，可能会损害人口和数据驱动的研究，例如机器学习。鉴于生物医学研究存储库中越来越多地包含来自 EHR 的大量历史数据，因此需要特定的软件方法来帮助描绘时间数据集的变化，以确保可靠的数据重用。