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Data de-identification often poses a challenge to researchers. With more funders supporting or requiring data sharing, data de-identification is a crucial step in the data management lifecycle. Explore openly available tools and technologies to help with data de-identification, including how machine learning and natural language processing apply to data de-identification.
Learn about the NLM Scrubber and other open NLP tools that can be used for clinical text de-identification. This session includes a demonstration of how to run NLM Scrubber and shows the types of data it can find and redact. Finally, the presenters will share ways to promote data privacy and data de-identification tools at your institution.
By registering for this class, you are agreeing to the NNLM Code of Conduct
This presentation addresses data and health information resources, as well as the NLM initiative of building a data-ready workforce by including information about de-identifying data and the NLM Scrubber resource.
NLM Scrubber Talking Points and Resources (Word)
1. Define artificial intelligence and natural language processing.
2. Compare NLM Scrubber and other clinical de-identification tools.
3. Implement open de-identification tools into library instruction and outreach programs.