Page History

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As a test run, I selected a small paragraph from the work-in-progress COVID-19 Interim User Guide. [3] This is a text about a data collection example for the disease's signs & and symptoms:

Data collection may include questions about groups of symptoms, such as

• GI symptoms (nausea, vomiting, diarrhea)
• Cough (non-productive, productive, or haemoptisis)

The next step was to extract named entities by running the signs & and symptoms text through the BC5CDR biomedical model, A named entity is text with a label of name of things. For BC5CDR, the entity types are DISEASE and CHEMICAL. This process is often referred to as named entity recognition (NER). These are the results:

The first surprise was a programmatic method to link discovered entity with Unified Medical Language System (UMLS). UMLS is maintained by U.S. National Library of Medicine (NLM). This is appealing, when a term or concept is curated in the UMLS, a formal definition exists. Each concept in the UMLS has a Concept Unique Identifier (CUI). [4] This process is typically called named entity linking (NEL).

Let's take a look at the outcome, with green shaded rows indicating my preferred match:

Notice how the table above does not include any UMLS concept for the named entity haemoptisis. With some online searches, Ii it came to me as another surprise that it is due to a typographical error. After correcting it to "hemoptysis," , a hit appears in the outcome, as follows:

Suffice to mention, these CUIs are available on the NCI Metathesaurus. This is the URL template: https://ncim.nci.nih.gov/ncimbrowser/ConceptReport.jsp?dictionary=NCI%20Metathesaurus&code={CUI}

Visualization

spacy has spaCy includes built in visualization constructors to display part-of-speech tags and syntactic dependencies. This The following  graphic is the rendition using the text described above:

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We discussed named entity recognition, which can be displayed as such:

Example Code

import scispacy
import spacy
from scispacy.umls_linking import UmlsEntityLinker
from spacy import displacy

nlp = spacy.load("en_ner_bc5cdr_md")

linker = UmlsEntityLinker(resolve_abbreviations=True)
nlp.add_pipe(linker)

text = """
Data collection may include questions about groups of symptoms, such as
  GI symptoms (nausea, vomiting, diarrhea)
  Cough (non-productive, productive, or haemoptisis)
"""

doc = nlp(text)

entities = doc.ents
for entity in entities:
    print(entity.text, entity.start_char, entity.end_char, entity.label_)

    for umls_ent in entity._.umls_ents:
        # tuple with 2 values
        conceptId, score = umls_ent

        print(f"Name: {entity}")
        print(f"CUI: {conceptId}, Score {score}")
        print(linker.umls.cui_to_entity[umls_ent[0]])
        print()

colors = {
    'CHEMICAL': 'lightpink',
    'DISEASE': 'lightorange',
}

# show NER
displacy.serve(doc, style="ent", host="127.0.0.1", options={'colors': colors})
displacy.serve(doc, style="dep", host="127.0.0.1")

<script src="https://bitbucket.cdisc.org/snippets/9817e4728bb1465ea8c338685ea454eb.js"></script>

The Road Ahead

At this point, there seems to be a lot NP possibilities of NLP opportunities and applications in standards development. Linkage to UMLS will allow team members to ensure semantic meaning by referencing the curated definition. Quality will increase as I demonstrated how detecting the spelling error detection was an unintended experience. I can certainly see it has a utility in CDISC 360's biomedical concept authoring. Named entities can be used as keywords or tags in Example Collection. 

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[3] CDISC Interim User Guide for COVID-19. https://wikiwww.cdisc.org/display/COVID19interim-user-guide-covid-19/

[4] Unique Identifiers in the Metathesaurus. https://www.nlm.nih.gov/research/umls/new_users/online_learning/Meta_005.html