real world data electronic health record database (Cerner Corporation)
86
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Cerner Corporation
real world data electronic health record database
Real World Data Electronic Health Record Database, supplied by Cerner Corporation, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/electronic+database/cerner+data+real+world/pm42128415-0-8-7
Average 86 stars, based on 1 article reviews
Real World Data Electronic Health Record Database, supplied by Cerner Corporation, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/electronic+database/cerner+data+real+world/pm42128415-0-8-7
Average 86 stars, based on 1 article reviews
real world data electronic health record database - by Bioz Stars,
2026-09
86/100 stars
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other:Article Title: GLP-1 Receptor Agonists in Psychiatry: A Pharmacoepidemiological Scoping Review. Article Snippet: This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability.. This version will undergo additional copyediting, typesetting and review before it is published in its final form.. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Article Title: AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges Article Snippet: Oracle Real-World Data (ORWD), formerly Article Title: Characteristics of Hemorrhagic Myocardial Infarction After ST-Segment Elevation Myocardial Infarction in the United States Article Snippet: We conducted this retrospective cohort study by using Article Title: A joint learning framework for analyzing data from national geriatric centralized networks: A new toolbox deciphering real-world complexity. Article Snippet: This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability.. This version will undergo additional copyediting, typesetting and review before it is published in its final form.. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Article Title: Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records. Article Snippet: Background: Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including posttraumatic epilepsy (PTE), a debilitating condition associated with significant long-term consequences.. The prognosis of PTE occurrence remains challenging due to the complex pathophysiology of PTE and the impracticality of traditional blood biomarkeror imaging-based screening for large populations.. This study proposes a graph-based deep learning approach that leverages electronic health records (EHR) to enhance the predictive assessment of PTE risk. Article Title: AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges. Article Snippet: Oracle Real-World Data (ORWD), formerly Vaccines:Article Title: Impact of the COVID-19 Pandemic on National Pediatric Inpatient Vaccine Delivery. Article Snippet: METHODS: In this retrospective multicenter study, the Cerner Real-World Data electronic health record database was used to determine pediatric inpatient vaccine delivery rates for the prepandemic (May 2017 to April 2020) and postpandemic (May 2020 to April 2023) periods.. Time plots were constructed to view vaccine delivery longitudinally, and interrupted time series analysis evaluated variation in slope of vaccine delivery rates between the periods while controlling for demographic, patient-level, and health system characteristics.. RESULTS: A total of 5 478 192 patient encounters across 91 health systems were included. |