Pipelines overview
EDS-NLP provides easy-to-use spaCy components.
| Pipeline | Description |
|---|---|
eds.normalizer |
Non-destructive input text normalisation |
eds.sentences |
Better sentence boundary detection |
eds.matcher |
A simple yet powerful entity extractor |
eds.terminology |
A simple yet powerful terminology matcher |
eds.contextual-matcher |
A conditional entity extractor |
eds.endlines |
An unsupervised model to classify each end line |
| Pipeline | Description |
|---|---|
eds.negation |
Rule-based negation detection |
eds.family |
Rule-based family context detection |
eds.hypothesis |
Rule-based speculation detection |
eds.reported_speech |
Rule-based reported speech detection |
eds.history |
Rule-based medical history detection |
| Pipeline | Description |
|---|---|
eds.dates |
Date extraction and normalisation |
eds.measurements |
Measure extraction and normalisation |
eds.sections |
Section detection |
eds.reason |
Rule-based hospitalisation reason detection |
| Pipeline | Description |
|---|---|
eds.covid |
A COVID mentions detector |
eds.charlson |
A Charlson score extractor |
eds.sofa |
A SOFA score extractor |
eds.emergency.priority |
A priority score extractor |
eds.emergency.ccmu |
A CCMU score extractor |
eds.emergency.gemsa |
A GEMSA score extractor |
eds.TNM |
A TNM score extractor |
eds.cim10 |
A CIM10 terminology matcher |
eds.drugs |
A Drug mentions extractor |
eds.adicap |
A ADICAP codes extractor |
You can add them to your spaCy pipeline by simply calling add_pipe, for instance:
# ↑ Omitted code that defines the nlp object ↑
nlp.add_pipe("eds.normalizer")