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May 7, 2020Open Access

Generation and evaluation of synthetic patient data

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Abstract

We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

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Authors

André Gonçalves

Lawrence Livermore National Laboratory

Priyadip Ray

Lawrence Livermore National Laboratory

Braden Soper

Lawrence Livermore National Laboratory

Journals

SHILAP Revista de lepidopterología

BMC Medical Research Methodology

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Lawrence Livermore National Laboratory

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Gonçalves et al. (Thu,) studied this question.

synapsesocial.com/papers/69dff744bdd89ea531860853 — DOI: https://doi.org/10.1186/s12874-020-00977-1

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Data-driven approach for creating synthetic electronic medical records· 2010 · 90 citations
A Case Study of the Impact of Statistical Disclosure Control on Data Quality in the Individual UK Samples of Anonymised Records· 2007 · 38 citations

Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

On the Hardness and Approximation of Euclidean DBSCAN· 2017 · 82 citations
A Systematic Review of Re-Identification Attacks on Health Data· 2011 · 430 citations
Boosting and Differential Privacy· 2010 · 783 citations
Data-driven approach for creating synthetic electronic medical records· 2010 · 90 citations
A Case Study of the Impact of Statistical Disclosure Control on Data Quality in the Individual UK Samples of Anonymised Records· 2007 · 38 citations

Generation and evaluation of synthetic patient data

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Abstract

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Authors

Journals

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References and Citations

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Discussion

Cite this study

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