Scores Overview

EDS-NLP provides multiple matchers for typical scores (Charlson, SOFA...) found in clinical documents. To extract a score, the matcher:

• extracts the score's name via the provided regular expressions
• extracts the score's raw value via another set of RegEx
• normalize the score's value via a normalising function

Available scores

Implementing your own score

Using the eds.score pipeline, you only have to change its configuration in order to implement a simple score extraction algorithm. As an example, let us see the configuration used for the eds.charlson pipe The configuration consists of 4 items:

• score_name: The name of the score
• regex: A list of regular expression to detect the score's mention
• value_extract: A regular expression to extract the score's value in the context of the score's mention
• score_normalization: A function name used to normalise the score's raw value

import spacy


@spacy.registry.misc("score_normalization.charlson")
def my_normalization_score(raw_score: str):
    # Implement some filtering here
    # Return None if you want the score to be discarded
    return normalized_score

The values used for the eds.charlson pipe are the following:

import spacy


@spacy.registry.misc("score_normalization.charlson")
def score_normalization(extracted_score):
    """
    Charlson score normalization.
    If available, returns the integer value of the Charlson score.
    """
    score_range = list(range(0, 30))
    if (extracted_score is not None) and (int(extracted_score) in score_range):
        return int(extracted_score)


charlson_config = dict(
    score_name="charlson",
    regex=[r"charlson"],
    value_extract=r"charlson.*[\n\W]*(\d+)",
    score_normalization="score_normalization.charlson",
)