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Generation of realistic synthetic validation healthcare datasets using generative adversarial networks
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Bilici Ozyigit, Eda, Arvanitis, Theodoros N. and Despotou, George (2020) Generation of realistic synthetic validation healthcare datasets using generative adversarial networks. In: The Importance of Health Informatics in Public Health during a Pandemic. Studies in Health Technology and Informatics, 272 . I O S Press, pp. 322-325. ISBN 9781643680927
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WRAP-generation-realistic-synthetic-validation-healthcare-datasets-generative-adversarial-networks-Arvanitis-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (403Kb) | Preview |
Official URL: http://dx.doi.org/10.3233/SHTI200560
Abstract
Background:
Assurance of digital health interventions involves, amongst others, clinical validation, which requires large datasets to test the application in realistic clinical scenarios. Development of such datasets is time consuming and challenging in terms of maintaining patient anonymity and consent.
Objective:
The development of synthetic datasets that maintain the statistical properties of the real datasets.
Method:
An artificial neural network based, generative adversarial network was implemented and trained, using numerical and categorical variables, including ICD-9 codes from the MIMIC III dataset, to produce a synthetic dataset.
Results:
The synthetic dataset, exhibits a correlation matrix highly similar to the real dataset, good Jaccard similarity and passing the KS test.
Conclusions:
The proof of concept was successful with the approach being promising for further work.
| Item Type: | Book Item | ||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Medical care -- Technological innovations, Medical informatics, Wireless communication systems in medical care , Machine learning , Data sets, Data protection | ||||||
| Series Name: | Studies in Health Technology and Informatics | ||||||
| Publisher: | I O S Press | ||||||
| ISBN: | 9781643680927 | ||||||
| ISSN: | 0926-9630 | ||||||
| Book Title: | The Importance of Health Informatics in Public Health during a Pandemic | ||||||
| Official Date: | 2020 | ||||||
| Dates: |
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| Volume: | 272 | ||||||
| Page Range: | pp. 322-325 | ||||||
| DOI: | 10.3233/SHTI200560 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 6 July 2020 | ||||||
| Date of first compliant Open Access: | 6 July 2020 | ||||||
| RIOXX Funder/Project Grant: |
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