{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:34:03Z","timestamp":1791426843577,"version":"4.3.3"},"reference-count":35,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T00:00:00Z","timestamp":1621987200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012553","name":"Chang Gung Memorial Hospital","doi-asserted-by":"crossref","award":["CMRPD3K0011"],"award-info":[{"award-number":["CMRPD3K0011"]}],"id":[{"id":"10.13039\/100012553","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST 109-2636-E-008-009"],"award-info":[{"award-number":["MOST 109-2636-E-008-009"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"crossref","award":["MOST 110-2636-E-008-008"],"award-info":[{"award-number":["MOST 110-2636-E-008-008"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The\n                      <jats:italic>dxpr<\/jats:italic>\n                      R package provides mechanisms for integration, wrangling, and visualization of clinical data, including diagnosis and procedure records. First, the\n                      <jats:italic>dxpr<\/jats:italic>\n                      package helps users transform International Classification of Diseases (ICD) codes to a uniform format. After code format transformation, the\n                      <jats:italic>dxpr<\/jats:italic>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>dxpr<\/jats:italic>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This\n                      <jats:italic>dxpr<\/jats:italic>\n                      package helps clinical data analysts address the common bottleneck caused by clinical data characteristics such as heterogeneity and sparseness.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj-cs.520","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T04:51:04Z","timestamp":1622004664000},"page":"e520","source":"Crossref","is-referenced-by-count":6,"title":["<i>dxpr<\/i>\n                    : an R package for generating 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