#2204. The national COVID cohort collaborative: Analyses of original and computationally derived electronic health record data

July 2026publication date
Proposal available till 30-05-2025
5 total number of authors per manuscript3530 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Sociology and Political Science;
Communication;
Health Science
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Abstract:
We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes.We used the National COVID Cohort Collaborative’s instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19–positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19–related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data.
Keywords:
COVID-19; Data analysis; Electronic health records and systems; Protected health information; Synthetic data

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