Improving health data quality by assessing and enhancing semantic integrity

随着术语的变化并随着时间的推移被不同的实体使用，可以发展 使用单一代码或一组代码所代表的变化和分歧。这些 可以从添加新的、更特殊的含义到代码集(例如，添加另一个 其含义最初被列为NEC(非其他分类)代码的代码的可能性，以 重大版本更改(如从ICD-9 cm到ICD-10 cm的过渡)，并进行本地适应 (例如，使用更通用的代码来指示机构的更具体情况。)是这样的 代码语义的改变带来了可以称为表征性的挑战 语义完整性(RS完整性)。如果多个代码或多个代码组合可以 代表相同的表型、队列识别或队列变量赋值 代码变得有问题了。由于许多研究项目都使用大型电子健康 包含标准化术语代码、违反RS完整性的记录(EHR)数据集 预计将在随后的分析和调查结果中传播错误。建议数 该项目致力于解决以下问题：如何评估和改进 使用自动化方法的纵向和异质电子病历数据？我们建议 开发新的数据驱动方法来分析时间模式和 EHR变量。以ICD-9 cm、ICD-10 cm、CPT和SNOMED代码为使用案例， 研究将利用非常大的、纵向的、异质的数据集：临床数据仓库 退伍军人管理局(VA)S国家电子病历系统，Cerner Real World Data(RWD) 和来自阿拉巴马大学大型医疗中心的电子病历数据存储库 伯明翰(UAB)。我们的目标是：1)开发数据驱动的方法来评估RS 纵向电子病历数据的完整性。我们将开发统计和深度学习模型来 执行多变量时间序列分析以检测代码中的异常信号 在EHR记录中；2)开发数据驱动的方法，以提高纵向RS的完整性 电子病历数据。我们将分析随时间推移的代码上下文以及跨数据源的 技术，并开发了一个生成语义的语义匹配工具 来自不同时间段和设施的数据的等效集群；以及3)验证 针对不同编码集和数据源的评估和改进方法。我们 还将评估对预测性建模的影响。

As terminologies change and are used over time by different entities, there can develop changes and divergence in what the use of a single code or a set of codes represent. These can range from adding a new, more special meaning, to a code set (e.g., adding another possibility to codes whose meaning was first listed as a NEC (not elsewhere classified) code, to a major version change (as in the transition from ICD-9CM to ICD-10CM), and to local adaption (e.g. using a more general code to indicate a more specific condition by an institution.) Such altering of the semantics of codes presents a challenge that can be termed representational semantic integrity (RS integrity). If multiple codes or multiple combinations of codes can represent the same phenotype, cohort identification or cohort variable assignment based on the codes becomes problematic. As numerous research projects utilize large electronic health record (EHR) datasets containing standardized terminology codes, violations of RS integrity would be expected to propagate errors in subsequent analyses and findings. The proposed project seeks to address the question: How to assess and improve RS integrity in longitudinal and heterogenous EHR data using automated methods? We propose to develop novel data driven methods to analyze the temporal pattern and the context of EHR variables. Using ICD-9CM, ICD-10CM, CPT and SNOMED codes as our use cases, this study will leverage very large, longitudinal, heterogenous datasets: the Clinical Data Warehouse of the Veteran Administration (VA)’s national EHR system, the Cerner Real World Data (RWD) and the EHR data repository from a large medical center at University of Alabama at Birmingham (UAB). Our aims are: 1) Develop data-driven approaches to assess RS integrity in longitudinal EHR data. We will develop statistical and deep learning models to perform multivariate time-series analysis for the purpose of detecting aberrant signals in codes in EHR records; 2) Develop data-driven approaches to improve RS integrity in longitudinal EHR data. We will analyze the contexts of codes over time and across data sources using embedding techniques and develop a semantic matching tool that generates semantic equivalent clusters for data from different time periods and facilities; and 3) Validate the assessment and improvement approaches on different coding sets and data sources. We will also assess the impact on predicative modeling.