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We analysed the performance of the pipeline eds.charlson over 100 documents extracted from the Bordeaux CHU medical datawarehouse. We compare Charlson score extracted by edsnlp pipeline with Charlson score extracted by hand. Over the hundred documents we found 5 diverging cases which brings out several issues that might be usefull in a more general context of integer score detection.
Proposition
Here are few points that could help to enhance score detection:
Include Roman numerals (i.e 'Charlson score is about II)
Ranges in score (i.e 'Charlson score lies between 2 and 3)
Fuzziness for mispelling score name (i.e 'Charltson score of 3')
Ordering (i.e 'Charlson score > 7)
The text was updated successfully, but these errors were encountered:
Camco3
changed the title
Feature request: [feature]
Feature request: Score
Apr 26, 2022
Thanks for the heads up! A few thoughts on this, for future reference:
spaCy's is_num attribute could be helpful there
We could draw inspiration from the eds.measures pipeline to capture these cases, I figure this is related to the concept of composite measures
I admit I'm a bit concerned about optimality there... As discussed, perhaps we should include these typos directly? I reckon the precision shouldn't suffer, what do you think?
I don't have much to add on this, it should definitely be handled
Score
Description
We analysed the performance of the pipeline
eds.charlson
over 100 documents extracted from the Bordeaux CHU medical datawarehouse. We compare Charlson score extracted by edsnlp pipeline with Charlson score extracted by hand. Over the hundred documents we found 5 diverging cases which brings out several issues that might be usefull in a more general context of integer score detection.Proposition
Here are few points that could help to enhance score detection:
The text was updated successfully, but these errors were encountered: