https://github.com/kavgan/clinical-concepts

Discovering Related Clinical Concepts using Large Amounts of Clinical Notes. An unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes.
https://github.com/kavgan/clinical-concepts

clinical-concepts clinical-nlp clinical-notes concept-graph graph-nlp nlp paper terminologies

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Discovering Related Clinical Concepts using Large Amounts of Clinical Notes. An unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes.

• Host: GitHub
• URL: https://github.com/kavgan/clinical-concepts
• Owner: kavgan
• License: gpl-3.0
• Created: 2016-06-13T17:53:55.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-22T05:22:59.000Z (almost 7 years ago)
• Last Synced: 2023-03-05T08:29:14.948Z (over 1 year ago)
• Topics: clinical-concepts, clinical-nlp, clinical-notes, concept-graph, graph-nlp, nlp, paper, terminologies
• Homepage: http://kavita-ganesan.com/discovering-related-clinical-concepts-using-large-amounts-of-clinical-notes/
• Size: 15.6 KB
• Stars: 16
• Watchers: 4
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Abstract

The ability to find highly related clinical concepts is essential for many applications such as for hypothesis generation, query expansion for medical literature search, search results filtering, ICD-10 code filtering and many other applications. While manually constructed medical terminologies such as SNOMED CT can surface certain related concepts, these terminologies are inadequate as they depend on expertise of several subject matter experts making the terminology curation process open to geographic and language bias. In addition, these terminologies also provide no quantifiable evidence on how related the concepts are. In this work, we explore an unsupervised graphical approach to mine related concepts by leveraging the volume within large amounts of clinical notes. Our evaluation shows that we are able to use a data driven approach to discovering highly related concepts for various search terms including medications, symptoms and diseases.

Mining Related Concepts

The Concept-Graph is used to mine related clinical terminology. For example if the query term is `advair`, related concepts can be `singulair`, `combivent`, `inhaler`, `nebs`, etc.  The Concept-Graph is an undirected graph with each node representing a concept and the link between the nodes indicate a presence of relationship between two concepts. The results of this work was evaluated by experts in the medical field. 


Links



Paper



Journal link



stop words used


Example Related Concepts

Concepts related to `chest pain`



Citation

@Article{Ganesan2016,

author={Ganesan, Kavita

and Lloyd, Shane

and Sarkar, Vikren},

title={Discovering Related Clinical Concepts Using Large Amounts of Clinical Notes},

journal={Biomed Eng Comput Biol},

year={2016},

month={Sep},

day={07},

publisher={Libertas Academica},

volume={7},

number={Suppl 2},

pages={27-33},

note={becb-suppl.2-2016-027[PII]},

note={27656096[pmid]},

issn={1179-5972},

doi={10.4137/BECB.S36155},

url={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5015701/}

}