Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies.
Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies.
复制标题
DOI：
10.1007/s10654-020-00677-6
复制
发表时间：
2021-03
影响因子：
13.6
通讯作者：
Nirantharakumar K
中科院分区：
文献类型：
--
作者：
Gokhale KM;Chandan JS;Toulis K;Gkoutos G;Tino P;Nirantharakumar K
使用初级保健电子健康记录进行研究是丰富的。利用这些记录的好处在于其规模、纵向数据收集和数据质量。然而，使用这些数据进行高质量的流行病学研究可能会带来重大挑战，特别是在处理分类错误、编码变化以及以有意义的统计分析格式预处理数据所需的大量工作方面。在本文中，我们描述了一种方法来帮助提取和处理这样的数据库，提供了一个新的软件程序，“数据提取流行病学研究”（DExtER）。DExtER的基础依赖于提取、转换和加载过程的原理。该工具最初提供提取医疗保健数据集的能力，然后以数据标准化，转换和重新格式化的格式进行转换。DExtER有一个用户界面，旨在获取每个研究问题和观察性研究设计的特定数据提取。有设施输入要求;合格的研究阶段，暴露和未暴露组的定义，结局指标和重要的基线协变量。迄今为止，该工具已在多种环境中得到使用和验证。已有超过35篇同行评审出版物使用该工具，DExtER已被实施为经过验证的公共卫生监测工具，用于获取关键疾病流行病学的准确统计数据。这项工作的未来方向将是将该框架应用于相互联系的数据集和国际数据集，并制定标准化方法，用于为研究目的进行电子预处理和从数据集提取。本文的在线版本（10.1007/s10654-020-00677-6）包含补充材料，可供授权用户使用。
The use of primary care electronic health records for research is abundant. The benefits gained from utilising such records lies in their size, longitudinal data collection and data quality. However, the use of such data to undertake high quality epidemiological studies, can lead to significant challenges particularly in dealing with misclassification, variation in coding and the significant effort required to pre-process the data in a meaningful format for statistical analysis. In this paper, we describe a methodology to aid with the extraction and processing of such databases, delivered by a novel software programme; the “Data extraction for epidemiological research” (DExtER). The basis of DExtER relies on principles of extract, transform and load processes. The tool initially provides the ability for the healthcare dataset to be extracted, then transformed in a format whereby data is normalised, converted and reformatted. DExtER has a user interface designed to obtain data extracts specific to each research question and observational study design. There are facilities to input the requirements for; eligible study period, definition of exposed and unexposed groups, outcome measures and important baseline covariates. To date the tool has been utilised and validated in a multitude of settings. There have been over 35 peer-reviewed publications using the tool, and DExtER has been implemented as a validated public health surveillance tool for obtaining accurate statistics on epidemiology of key morbidities. Future direction of this work will be the application of the framework to linked as well as international datasets and the development of standardised methods for conducting electronic pre-processing and extraction from datasets for research purposes. The online version of this article (10.1007/s10654-020-00677-6) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
8.2
作者：
Dafoulas GE;Toulis KA;Mccorry D;Kumarendran B;Thomas GN;Willis BH;Gokhale K;Gkoutos G;Narendran P;Nirantharakumar K
通讯作者：
Nirantharakumar K
影响因子：
8.2
作者：
Chou, I-Ching;Wang, Chung-Hsing;Kao, Chia-Hung
通讯作者：
Kao, Chia-Hung
影响因子：
5.7
作者：
Adderley, Nicola Jaime;Ryan, Ronan;Marshall, Tom
通讯作者：
Marshall, Tom
影响因子：
2.6
作者：
Horsfall, Laura;Walters, Kate;Petersen, Irene
通讯作者：
Petersen, Irene
影响因子：
2.6
作者：
Maguire, Andrew;Blak, Betina T.;Thompson, Mary
通讯作者：
Thompson, Mary