SOFA

The eds.sofa component extracts Sequential Organ Failure Assessment (SOFA) scores, used to track a person's status during the stay in an intensive care unit to determine the extent of a person's organ function or rate failure.

Examples

import edsnlp

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

text = """
SOFA (à 24H) : 12.
OMS:
"""

doc = nlp(text)
doc.ents
# Out: (SOFA (à 24H) : 12,)

Extensions

Each extraction exposes 3 extensions:

ent = doc.ents[0]

ent._.score_name
# Out: 'sofa'

ent._.score_value
# Out: 12

ent._.score_method
# Out: '24H'

Score method can here be "24H", "Maximum", "A l'admission" or "Non précisée"

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline object TYPE: `PipelineProtocol`
`name`	The name of the component TYPE: `Optional[str]` DEFAULT: `None`
`regex`	A list of regexes to identify the SOFA score TYPE: `List[str]` DEFAULT: `regex`
`attr`	Whether to match on the text ('TEXT') or on the normalized text ('CUSTOM_NORM') TYPE: `str` DEFAULT: `'NORM'`
`value_extract`	Regex to extract the score value TYPE: `Dict[str, 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: `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: `10`
`ignore_excluded`	Whether to ignore excluded spans TYPE: `bool` DEFAULT: `False`
`ignore_space_tokens`	Whether to ignore space tokens TYPE: `bool` DEFAULT: `False`
`flags`	Flags to pass to the regex TYPE: `Union[RegexFlag, int]` DEFAULT: `0`
`label`	Label name to use for the `Span` object and the extension TYPE: `str` DEFAULT: `'sofa'`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'sofa': True}`