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.