PubMed Comments - Similarity of Published Articles

Researchers require finding other published materials to help determine supporting evidence associated  with an internal hypothesis in specific functional terms under study.  A research team will have specific term set.  Some terms will match and some terms won't and it is that detailed differences that helps their endeavor. 

PubMed is a great resource built over the years to return matches to keyword search.  In many domains groups collaborate on naming conventions sometimes captured in ontological formats.

MESH - https://meshb.nlm.nih.gov/search - is one of many standards within the Medical community in use of proper terminology when reporting or publishing articles.

LOINC - https://loinc.org/ - is another mapping standard

HL7 - 

And other datasets like:

  • Searchable databases - CPT, Rx, ICD9
  • Access to Therapeutic Specific Databases
  • Data aggregation from EMR, claims, Rx data
  • as listed by AdviseClinical LLC - 
  • They are associates of Dr Galpin - a person effected by Polio, that helped resolve Polio 
  • Dr Galpin and his team created technical works supporting therapeutic support:
  • Recent research
  • He is real world Dr Sheldon Cooper - graduating with PhD when he was 16 from Chicago and working at Argonne National Labs.

  • I spent a few months working with the team on informatics approaches. 


There are many other terminology standards associated with functional specialists. 

Matches on official terms contained in the abstract and other textual sources enable similarity searches.  

Similarity is tough to get right across all the functional specialists in many domains because individual knowledge based mental maps are not easily mapped consistently against the standards.

In the Memex example below - a particular similarity between articles - regarding liver duress within the pandemic settings - returns 9 close matches.  

The same set of terms placed into PubMed returns only one article and none of the similar articles match the initial any of the articles matched by the default Elasticsearch "more like this" query.  

According to published articles regarding PubMed's similarity algorithm, paper citations embedded in actual article text associated with specific paragraphs are utilized to find similar documents. 

Ideally, a research group can apply the same term set against the entire publication set and find the specific terms supporting their hypothesis.  Unfortunately, as it exists current PubMed lacks this fundamental capability.   

In the next post, I will use Elasticsearch visualizations to show the commons and the differences between items sets.

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