dxpr: an R package for generating analysis-ready data from electronic health records—diagnoses and procedures
| Autor: | Chun Ju Chen, Hsiang Ju Chiu, Yi Ju Tseng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Bioinformatics Field (computer science) Set (abstract data type) 03 medical and health sciences Databases 0302 clinical medicine Exploratory data analysis Health care medicine Electronic health records 030212 general & internal medicine Medical diagnosis 030304 developmental biology Analysis-ready data 0303 health sciences business.industry Data Science R package Software Engineering QA75.5-76.95 medicine.disease Comorbidity Visualization Electronic computers. Computer science Data analysis Medical emergency business |
| Zdroj: | PeerJ Computer Science PeerJ Computer Science, Vol 7, p e520 (2021) |
| ISSN: | 2376-5992 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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