Published on in Vol 8, No 2 (2020): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16492, first published .
Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies

Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies

Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies

Journals

  1. Rankin D, Black M, Bond R, Wallace J, Mulvenna M, Epelde G. Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. JMIR Medical Informatics 2020;8(7):e18910 View
  2. Gillies C, Taylor D, Cummings B, Ansari S, Islim F, Kronick S, Medlin R, Ward K. Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution. Journal of Biomedical Informatics 2020;110:103528 View
  3. Azizi Z, Zheng C, Mosquera L, Pilote L, El Emam K. Can synthetic data be a proxy for real clinical trial data? A validation study. BMJ Open 2021;11(4):e043497 View
  4. Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, Godtliebsen F. Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics 2021 View
  5. Vourganas I, Stankovic V, Stankovic L. Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation. Sensors 2020;21(1):2 View
  6. Jeon S, Seo J, Kim S, Lee J, Kim J, Sohn J, Moon J, Joo H. Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models. Journal of Medical Internet Research 2020;22(11):e19597 View
  7. Dankar F, Ibrahim M. Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation. Applied Sciences 2021;11(5):2158 View
  8. Foraker R, Yu S, Gupta A, Michelson A, Pineda Soto J, Colvin R, Loh F, Kollef M, Maddox T, Evanoff B, Dror H, Zamstein N, Lai A, Payne P. Spot the difference: comparing results of analyses from real patient data and synthetic derivatives. JAMIA Open 2021;3(4):557 View
  9. Korytny A, Klein A, Marcusohn E, Freund Y, Neuberger A, Raz A, Miller A, Epstein D. Hypocalcemia is associated with adverse clinical course in patients with upper gastrointestinal bleeding. Internal and Emergency Medicine 2021 View
  10. Kaur D, Sobiesk M, Patil S, Liu J, Bhagat P, Gupta A, Markuzon N. Application of Bayesian networks to generate synthetic health data. Journal of the American Medical Informatics Association 2021;28(4):801 View
  11. Epstein D, Solomon N, Korytny A, Marcusohn E, Freund Y, Avrahami R, Neuberger A, Raz A, Miller A. Association between ionised calcium and severity of postpartum haemorrhage: a retrospective cohort study. British Journal of Anaesthesia 2021;126(5):1022 View
  12. Maweu B, Shamsuddin R, Dakshit S, Prabhakaran B. Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks. IEEE Transactions on Instrumentation and Measurement 2021;70:1 View
  13. Pereira T, Morgado J, Silva F, Pelter M, Dias V, Barros R, Freitas C, Negrão E, Flor de Lima B, Correia da Silva M, Madureira A, Ramos I, Hespanhol V, Costa J, Cunha A, Oliveira H. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare 2021;9(7):827 View
  14. Weber Y, Epstein D, Miller A, Segal G, Berger G. Association of Low Alanine Aminotransferase Values with Extubation Failure in Adult Critically Ill Patients: A Retrospective Cohort Study. Journal of Clinical Medicine 2021;10(15):3282 View