Background Enriched electronic health records (EHRs) contain crucial information related to disease progression, and this information can help with decision-making in the health care field. Data analytics in health care is deemed as one of the essential processes that help accelerate the progress of clinical research. However, processing and analyzing EHR data are common bottlenecks in health care data analytics. Methods The dxpr R package provides mechanisms for integration, wrangling, and visualization of clinical data, including diagnosis and procedure records. First, the dxpr package helps users transform International Classification of Diseases (ICD) codes to a uniform format. After code format transformation, the dxpr package supports four strategies for grouping clinical diagnostic data. For clinical procedure data, two grouping methods can be chosen. After EHRs are integrated, users can employ a set of flexible built-in querying functions for dividing data into case and control groups by using specified criteria and splitting the data into before and after an event based on the record date. Subsequently, the structure of integrated long data can be converted into wide, analysis-ready data that are suitable for statistical analysis and visualization. Results We conducted comorbidity data processes based on a cohort of newborns from Medical Information Mart for Intensive Care-III (n = 7,833) by using the dxpr package. We first defined patent ductus arteriosus (PDA) cases as patients who had at least one PDA diagnosis (ICD, Ninth Revision, Clinical Modification [ICD-9-CM] 7470*). Controls were defined as patients who never had PDA diagnosis. In total, 381 and 7,452 patients with and without PDA, respectively, were included in our study population. Then, we grouped the diagnoses into defined comorbidities. Finally, we observed a statistically significant difference in 8 of the 16 comorbidities among patients with and without PDA, including fluid and electrolyte disorders, valvular disease, and others. Conclusions This dxpr package helps clinical data analysts address the common bottleneck caused by clinical data characteristics such as heterogeneity and sparseness.

中文翻译：

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dxpr：用于从电子健康记录生成分析就绪数据的 R 包——诊断和程序

背景 丰富的电子健康记录 (EHR) 包含与疾病进展相关的重要信息，这些信息可以帮助医疗保健领域的决策。医疗保健中的数据分析被认为是有助于加速临床研究进展的重要过程之一。然而，处理和分析 EHR 数据是医疗保健数据分析中的常见瓶颈。方法 dxpr R 软件包提供了用于临床数据（包括诊断和程序记录）的集成、整理和可视化的机制。首先，dxpr 包帮助用户将国际疾病分类 (ICD) 代码转换为统一格式。代码格式转换后，dxpr 包支持四种临床诊断数据分组策略。对于临床程序数据，可以选择两种分组方法。集成 EHR 后，用户可以使用一组灵活的内置查询功能，按照指定的条件将数据分为病例组和对照组，并根据记录日期将数据分为事件之前和之后。随后，集成长数据的结构可以转换为适用于统计分析和可视化的广泛的、可分析的数据。结果 我们使用 dxpr 软件包对来自 Medical Information Mart for Intensive Care-III (n = 7,833) 的一组新生儿进行了合并症数据处理。我们首先将动脉导管未闭 (PDA) 病例定义为至少有一次 PDA 诊断（ICD，第九版，临床修改 [ICD-9-CM] 7470*）的患者。对照组被定义为从未诊断过 PDA 的患者。总共，我们的研究人群分别包括 381 名和 7,452 名患有和不患有 PDA 的患者。然后，我们将诊断分为明确的合并症。最后，我们观察到有和没有 PDA 患者的 16 种合并症中有 8 种存在统计学上的显着差异，包括体液和电解质紊乱、瓣膜疾病等。结论 此 dxpr 软件包可帮助临床数据分析师解决由临床数据特征（如异质性和稀疏性）引起的常见瓶颈。和别的。结论 此 dxpr 软件包可帮助临床数据分析师解决由临床数据特征（如异质性和稀疏性）引起的常见瓶颈。和别的。结论 此 dxpr 软件包可帮助临床数据分析师解决由临床数据特征（如异质性和稀疏性）引起的常见瓶颈。