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Reference Docs
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News
Scrubber now uses Apache cTakes to provide parallel concept extraction during de-idenification. Apache cTAKES graciously invited us to port the Scrubber de-identification pipeline to the Apache hosted codebase. The maintenance version of the 2.X will remain available as will the 3.0 release candidate. The publication describing this work has been accepted, this site will be updated shortly to reflect the described methods and results.
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McMurry* AJ, Fitch* B, Savova G, Kohane IS, Reis BY. “Improved de-identification of physician notes through integrative modeling of both identifying and non-identifying medical text”, BMC Medical Informatics and Decision Making Accepted minor revise Jan 2013.
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Overview
3.X is a new vision for the scrubber. As we approached diminishing returns for improving REGEX and whitelists/black lists, we have shifted towards a machine learning methods approach and learning from large bodies of medical information from publications and UMLS dictionaries.
Venn Diagram
System Use Cases
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Use Case: Tagging Noun Phrases and UMLS concepts
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Precondition:
- Training Data
- Software: cTakes using features POS speech tagger & UMLS CUID extractor
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- Input document (either medical note OR publication) POS tagged and medical concept CUIDs.
Use Case: Meta-analysis of text
Precondition:
- Tagging Noun Phrases
- Scubber configured (with or without local dictionary/regex mods)
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- Text is processed by more than one algorithm "ham vs spam"
Example text block -> Feature Set
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