This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Big Data (240) Electronic Health Records (1053) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Clinical Informatics (1022) Secondary Use of Clinical Data for Research and Surveillance (366) Data Science (270) Datasets (10) Open Source and Data for Public Health (23)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30697, first published .
The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data

The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data

The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data

Authors of this article:

Randi Foraker1, 2 Author Orcid Image ;   Aixia Guo2 Author Orcid Image ;   Jason Thomas3 Author Orcid Image ;   Noa Zamstein4 Author Orcid Image ;   Philip RO Payne1, 2 Author Orcid Image ;   Adam Wilcox3 Author Orcid Image ;   N3C Collaborative5
Article Authors Cited by (10) Tweetations (9) Metrics

Journals

  1. El Emam K, Mosquera L, Fang X. Validating a membership disclosure metric for synthetic health data. JAMIA Open 2022;5(4) View
  2. Thomas J, Foraker R, Zamstein N, Morrow J, Payne P, Wilcox A, Haendel M, Chute C, Gersing K, Walden A, Bennett T, Eichmann D, Guinney J, Kibbe W, Liu H, Pfaff E, Robinson P, Saltz J, Spratt H, Starren J, Suver C, Williams A, Wu C, Gabriel D, Hong S, Kostka K, Lehmann H, Moffitt R, Morris M, Palchuk M, Zhang X, Zhu R, Amor B, Bissell M, Clark M, Girvin A, Lee A, Miller R, Walters K, Chae Y, Cook C, Dest A, Dietz R, Dillon T, Francis P, Fuentes R, Graves A, McMurry J, Neumann A, O'Neil S, Sheikh U, Volz A, Zampino E, Austin C, Bozzette S, Deacy M, Garbarini N, Kurilla M, Michael S, Rutter J, Temple-O'Connor M, Bradwell K, Manna A, Qureshi N, Saltz M, Bramante C, Harper J, Hernandez W, Koraishy F, Mariona F, Mattapally S, Saha A, Vedula S, Fu Y, Mathews N, Mendelevitch O. Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C). Journal of the American Medical Informatics Association 2022;29(8):1350 View
  3. Murtaza H, Ahmed M, Khan N, Murtaza G, Zafar S, Bano A. Synthetic data generation: State of the art in health care domain. Computer Science Review 2023;48:100546 View
  4. Theodorou B, Xiao C, Sun J. Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model. Nature Communications 2023;14(1) View
  5. Llewellyn N, Nehl E, Dave G, DiazGranados D, Flynn D, Fournier D, Hoyo V, Pelfrey C, Casey S. Translation in action: Influence, collaboration, and evolution of COVID‐19 research with Clinical and Translational Science Awards consortium support. Clinical and Translational Science 2024;17(1) View
  6. Lun R, Siegal D, Ramsay T, Stotts G, Dowlatshahi D, de Carvalho L. Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data. PLOS ONE 2024;19(2):e0295921 View
  7. El Emam K, Mosquera L, Fang X, El-Hussuna A. An evaluation of the replicability of analyses using synthetic health data. Scientific Reports 2024;14(1) View
  8. Wang E, Mott K, Zhang H, Gazit S, Chodick G, Burcu M. Validation Assessment of Privacy‐Preserving Synthetic Electronic Health Record Data: Comparison of Original Versus Synthetic Data on Real‐World COVID‐19 Vaccine Effectiveness. Pharmacoepidemiology and Drug Safety 2024;33(10) View

Books/Policy Documents

  1. Zamstein N, Nanyonga S, Morel E, Wayne R, Nottebaum S, Kozlakidis Z. Digitalization of Medicine in Low- and Middle-Income Countries. View