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UMLS

The eds.umls pipeline component matches the UMLS (Unified Medical Language System from NIH) terminology.

Very low recall

When using the exact matching mode, this component has a very poor recall performance. We can use the simstring mode to retrieve approximate matches, albeit at the cost of a significantly higher computation time.

Usage

eds.umls is an additional module that needs to be setup by:

  1. pip install -U umls_downloader
  2. Signing up for a UMLS Terminology Services Account. After filling a short form, you will receive your token API within a few days.
  3. Set UMLS_API_KEY locally: export UMLS_API_KEY=your_api_key
import spacy

nlp = spacy.blank("fr")
nlp.add_pipe("eds.umls")

text = "Grosse toux: le malade a été mordu par des Amphibiens " "sous le genou"

doc = nlp(text)

doc.ents
# Out: (toux, a, par, Amphibiens, genou)

ent = doc.ents[0]

ent.label_
# Out: umls

ent._.umls
# Out: C0010200

You can easily change the default languages and sources with the pattern_config argument:

import spacy

# Enable the french and english languages, through the french MeSH and LOINC
pattern_config = dict(languages=["FRE", "ENG"], sources=["MSHFRE", "LNC"])

nlp = spacy.blank("fr")
nlp.add_pipe("eds.umls", config=dict(pattern_config=pattern_config))

See more options of languages and sources here.

Configuration

The pipeline can be configured using the following parameters :

PARAMETER DESCRIPTION
attr

Attribute to match on, eg TEXT, NORM, etc.

TYPE: Union[str, Dict[str, str]] DEFAULT: 'NORM'

ignore_excluded

Whether to skip excluded tokens during matching.

TYPE: bool DEFAULT: False

ignore_space_tokens

Whether to skip space tokens during matching.

TYPE: bool DEFAULT: False

term_matcher

The term matcher to use, either TerminologyTermMatcher.exact or TerminologyTermMatcher.simstring

TYPE: TerminologyTermMatcher DEFAULT: TerminologyTermMatcher.exact

term_matcher_config

The configuration for the term matcher

TYPE: Dict[str, Any] DEFAULT: {}

pattern_config

The pattern retriever configuration

TYPE: Dict[str, Any] DEFAULT: dict(languages=['FRE'], sources=None)

Authors and citation

The eds.umls pipeline was developed by AP-HP's Data Science team and INRIA SODA's team.