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We are a group of doctors trying to use linguistic features of "Spacy", especially the part of speech tagging to show relationships between medical concepts like:

'Femoral artery pseudoaneurysm as in ==>

"femoral artery" ['Anatomical Location'] --> and "pseudoaneurysm" ['Pathology']

We are new to NLP and spacy, can someone with experience with NLP and Spacy explain if this is a good approach to show these relationships in medical documents? If not what are the other alternative methods?

Many thanks!

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Based on the example, it looks like you need more than simple POS tagging. Thankfully there is a full subdomain of NLP devoted to biomedical data, and there are many tools available which can help with this kind of task:

  • In case the data is made of biomedical research papers, you will find a lot of resources related to the Medline and PubMedCentral databases:
  • cTakes is another annotator system which is more specialized with clinical texts.
  • SciSpacy is a Spacy variant specialized for biomedical text. It can also annotate medical terms with UMLS labels.

The last one in particular seems particularly appropriate in your case. biomedical text presents a lot of specific difficulties which cannot be handled with general domain models.

Note that there are probably more tools and resources, this a very active domain.

(disclaimer: I recycled a large part of an older answer)

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  • $\begingroup$ Dear Erwan thanks for your answer. I had a look at your old answer as well. Very useful links. $\endgroup$ May 30, 2022 at 7:03

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