edsnlp.pipelines.ner.scores.sofa.patterns

regex = ['\\bsofa\\b'] module-attribute

digits = '[^\\d]*(\\d*)' module-attribute

after_extract = [dict(name='method_max', regex='sofa.*?(?:max{digits})'.format(digits=digits)), dict(name='method_24h', regex='sofa.*?(?:24h{digits})'.format(digits=digits)), dict(name='method_adm', regex='sofa.*?(?:admission{digits})'.format(digits=digits)), dict(name='no_method', regex='sofa.*?{digits}'.format(digits=digits))] module-attribute

score_normalization_str = 'score_normalization.sofa' module-attribute

score_normalization(extracted_score)

Sofa score normalization. If available, returns the integer value of the SOFA score.

Source code in edsnlp/pipelines/ner/scores/sofa/patterns.py

@spacy.registry.misc(score_normalization_str)
def score_normalization(extracted_score: Union[str, None]):
    """
    Sofa score normalization.
    If available, returns the integer value of the SOFA score.
    """
    value_regex = r".*?[\n\W]*?(\d+)"
    digit_value = re.match(
        value_regex, extracted_score
    )  # Use match instead of search to only look at the beginning
    digit_value = None if digit_value is None else digit_value.groups()[0]
    score_range = list(range(0, 30))
    if (digit_value is not None) and (int(digit_value) in score_range):
        return int(digit_value)