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  • MeSH on Demand identifies MeSH Terms in your text using the NLM Medical Text Indexer (MTI) program. After processing, MeSH on Demand returns a list of MeSH Terms relevant to your text. For more information about MeSH on Demand, please see our NLM Technical Bulletin article.
  • Please send your questions, suggestions, and comments to: NLMMESH-MOD@mail.nih.gov
  • Please Note: This tool is NOT intended for processing personally identifiable, sensitive or protected-health information. The system is not configured for secure communications. It is your responsibility to NOT submit any personally identifiable, sensitive or protected-health information.
  • MeSH on Demand does not retain or otherwise reuse any text submitted for processing.

 

Text to be Processed (Single block of text, maximum 10,000 characters):

Currently Identifying
2015 MeSH Terms




 


Helpful Hints:

  • Typical MTI processing takes approximately 30 - 45 seconds. Processing time depends on the amount of text provided - the more text, the longer MTI takes to process.
  • Provide well-defined sentences. Long lists without sentence breaks force MTI to take longer to process and may end without any results. If your input text contains Non-ASCII characters, an attempt is made to convert the text to ASCII before processing with MTI.
  • The results provided by MeSH on Demand are a simple list of MeSH Terms that MTI identifies as being relevant to your text. Each of the identified MeSH Terms has a link to the corresponding MeSH Browser Web page for that MeSH Term.


MeSH on Demand has been developed in close collaboration among Medical Subject Headings (MeSH) Section, NLM Index Section and the Lister Hill National Center for Biomedical Communications.

Full Disclaimer: The MeSH on Demand MeSH Terms are machine generated by MTI and DO NOT reflect any human review. MTI may recommend MeSH Terms not explicitly found in the text and may not recommend MeSH Terms that are in the text. This is a result of machine logic that attempts to emulate human indexer behavior in characterizing biomedically relevant parts of the text. These results will undoubtedly differ from any human-generated indexing.