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Accurate De-Identification of Structured & Unstructured Medical Data at Scale

Recent advances in deep learning enable automated de-identification of medical data to approach the accuracy achievable via manual effort. This includes accurate detection & obfuscation of patient names, doctor names, locations, organizations, and dates from unstructured documents – or accurate detection of column names & values in structured tables. This webinar explains:

  • What’s required to de-identify medical records under the US HIPAA de identification privacy rule
  • Typical de-identification use cases, for structured and unstructured data
  • How to implement de-identification of these use cases using Spark NLP for Healthcare

After the webinar, you will understand how to de-identify health information automatically, accurately, and at scale, for the most common scenarios.