Unlocking Distributed Health Data for Machine Learning

Data sharing barriers hinder AI in healthcare.

Data sensitivity, high volumes of data, and interoperability challenges prevent researchers from accessing sufficient data to feed their hungry algorithms and drive better patient outcomes.

Read this white paper to learn:

  • Why doing machine learning and analytics on distributed health data (e.g., EHR/EMR data) is challenging

  • How applying federated architecture can unlock insights from health data around the world, while protecting patient privacy
  • What the implications are for precision medicine, drug discovery, diagnostics, real-world evidence, and other use cases
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