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

Development and Evaluation of Research Methods, Instruments and Tools (630) Security in Digital Health (42) Big Data (278) Electronic Health Records (1169) Secondary Use of Clinical Data for Research and Surveillance (413) New Methods (1028) Advanced Data Analytics in eHealth (167)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53241, first published .
Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation

Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation

Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation

Authors of this article:

Elnaz Karimian Sichani1 Author Orcid Image ;   Aaron Smith1 Author Orcid Image ;   Khaled El Emam2, 3, 4 Author Orcid Image ;   Lucy Mosquera2, 3 Author Orcid Image
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JMIR Formative Res: Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation https://t.co/mxbA9v2lm6 https://t.co/ZzjWasV1Et

1:36 PM · Apr 22, 2024

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RT @jmirpub: JMIR Formative Res: Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation https://t.co/m…

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RT @jmirpub: JMIR Formative Res: Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation https://t.co/m…

1:44 PM · Apr 22, 2024

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