Charlson[source]

The eds.charlson component extracts the Charlson Comorbidity Index.

Examples

import edsnlp, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.sentences())
nlp.add_pipe(eds.normalizer())
nlp.add_pipe(eds.charlson())

text = """
Charlson à l'admission: 7.
Charlson:
OMS:
"""

doc = nlp(text)
doc.ents
# Out: (Charlson à l'admission: 7,)

We can see that only one occurrence was extracted. The second mention of Charlson in the text doesn't contain any numerical value, so it isn't extracted.

Extensions

Each extraction exposes 2 extensions:

ent = doc.ents[0]

ent._.score_name
# Out: 'charlson'

ent._.score_value
# Out: 7

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline object TYPE: `PipelineProtocol`
`name`	Name of the component TYPE: `Optional[str]` DEFAULT: `'charlson'`
`regex`	A list of regexes to identify the score TYPE: `List[str]` DEFAULT: `regex`
`attr`	Whether to match on the text ('TEXT') or on the normalized text ('NORM') TYPE: `str` DEFAULT: `'NORM'`
`value_extract`	Regex with capturing group to get the score value TYPE: `str` DEFAULT: `value_extract`
`score_normalization`	Function that takes the "raw" value extracted from the `value_extract` regex and should return: None if no score could be extracted The desired score value else TYPE: `Union[str, Callable[[Union[str, None]], Any]]` DEFAULT: `score_normalization_str`
`window`	Number of token to include after the score's mention to find the score's value TYPE: `int` DEFAULT: `7`
`ignore_excluded`	Whether to ignore excluded spans when matching TYPE: `bool` DEFAULT: `False`
`ignore_space_tokens`	Whether to ignore space tokens when matching TYPE: `bool` DEFAULT: `False`
`flags`	Regex flags to use when matching TYPE: `Union[RegexFlag, int]` DEFAULT: `0`
`label`	Label name to use for the `Span` object and the extension TYPE: `str` DEFAULT: `'charlson'`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'charlson': True}`

RETURNS	DESCRIPTION
`SimpleScoreMatcher`