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This meant people wishing to use the code after release could access and use the same dataset with which the project was developed. The teams chose to work from a starting dataset that was already in the public domain. To be able to increase SynthVAEвЂ™s range of capabilities, the teams needed an input dataset containing a number of different data types in order to broaden the range of the data produced.

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implement a structure to allow users to run the full functionality with a single piece of code.create a standard series of checks that can be carried out on the data produced, so that people can better understand its characteristics.increase the range of synthetic data types that SynthVAE can generate.If you would like greater technical detail about this project, please read the version on the Skunkworks Github website. The teams explored how SynthVAE could be used to generate synthetic data, how that data would be evaluated and how the whole process could be documented for others to re-use. In a partnership project, the NHS Transformation DirectorateвЂ™s Analytics Unit and the NHS AI Lab Skunkworks team sought to further improve an existing synthetic data generation model (called SynthVAE) and develop a framework for generating synthetic data that could be shared for others to re-use.

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This is a problem because it means a significant amount of work is needed to update them for use with a different data source.ДЄdditionally, once data has been produced, it can be difficult to know whether it is actually useful. One common challenge with synthetic data approaches is that they are usually configured specifically for a dataset. There are many ways to generate synthetic data.

Synthetic data aims to remove the need for such concerns because there is no вЂњreal patientвЂќ connected with the data.

Wide release of anonymised data will always carry some risks. However, it is very difficult to entirely remove the chance of re-identification. Various anonymisation techniques can be used to turn data into a form that does not directly identify individuals and where re-identification is not likely to take place. Using real patient data for research and development carries with it safety and privacy concerns about the anonymity of the people behind the information.

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