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SyntheaTM is an open-source, synthetic patient generator that models the medical history of synthetic patients. Our mission is to provide high-quality, synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions, enabling research with Health IT data that is otherwise legally or practically unavailable. The models used to generate synthetic patients are informed by...

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the...

We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid...

this tool for test-data-generation receives an 'excel-sheet' as primary input. second important paramter is the 'number of test-records to produce'. The excel-data will be reused as long data is needed. This tool is hightly paramatrisazable by the use of 'xsl scripts'. data can be created, updated, modified and finally exported in a format of your choice Main Fuctions: (1) Generates Testdata (excel, xsl, xml) (2) Exports generated testdata in multiple formats (csv, excel, html,...