Workshop: Generating Synthetic Data for Healthcare & Life Sciences

Enabling faster access to data for medical research with statistically accurate, equitable and private synthetic datasets.

Workshop: Generating Synthetic Data for Healthcare & Life Sciences
Copyright (c) Gretel 2021
“What we see is a change in the way that organizations are accessing and being able to work with data in the sense that we expect more and more from machine learning algorithms, as we have more and more devices that are gathering data, there's a couple big things that are changing. One due to the sensitivity of data – it's harder to enable access.” -Alex Watson

At Gretel when it comes to data we care a lot about ethical, equitable and fair practices. Gretel’s CPO Alex Watson, gave a workshop to a data science working group at Emory University on how you can address these practices, and how to leverage tools from Gretel to create statistically accurate synthetic data for health and life sciences research.

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In this workshop he covers the following topics:

  • Use cases for synthetic data in healthcare
    • Reducing bias in healthcare datasets
    • Generating more samples from limited datasets
    • Making private data shareable
  • Ethical, equity and fair representations of data in healthcare
  • What is synthetic data
  • Building a generative synthetic data model in 5 steps
  • Case study: Reducing bias in healthcare data
  • Comparing synthetic data versus original

“Synthetic data is annotated information that computer simulations or algorithms generate as an alternative to real-world data” -NVDIA

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