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Note : Scrubber 3.X is being ported to Apache cTAKES, this is an interim BETA release. |
1. Intended usages
1.1 Default configuration
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We recommend starting with the default properties and prebuilt train/test models. The train and test models are anonymized feature sets generated by scrubber runtime (NOT text). |
1.2 Customize NLP pipeline
- Scrubber uses Apache UIMA and Apache cTAKES packages, which together provide the NLP pipeline for lexical parsing and medical concept annotation. Generated feature sets are exported to the SQL database or model file (CSV, ARFF). The UIMA and cTAKES services used by Scrubber are defined and configured using scrubber.properties.
1.3 Customize Classifier
- Scrubber can use different classifier implementations without recompiling the software.
- By default scrubber dynamically loads the popular WEKA C4.5 decision tree classifier with multi-class support.
2. Software Features
2.1 Annotation
- Annotate word tokens and redact PHI from physician notes
- cTAKES lexical parsing and medical dictionary annotation
- WEKA multi-class decision tree classifier (plugin default)
- Protege UI support for human expert curators (reads output)
- Generate feature sets containing lexical properties, medical concept codes, and human defined rules
2.2 Models
- Prebuilt train and test models can be imported to Weka (default), Matlab, or R
- (default) Test your local physician notes without retraining
- (optional) Retrain model using local physician note samples, publications, and medical dictionaries.
2.3 Classification
- Distinguish (classify) private patient data from coded medical concepts and commonly used words
2.4 Compare Text
- Compare lexical properties and distributions of public and private text sources
3. How To
3.X Install / Train / Test / Scrub
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name | scrubber-3.x-runtime-guide.doc |
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Scrubber Property KEY = VALUE
4. scrubber.properties
4.1 Java Object
4.2 Java Template
4.3 Shell scripts
4.4 Shell UnitTest