Literature DB >> 21347043

Semantic Similarity and Relatedness between Clinical Terms: An Experimental Study.

Serguei Pakhomov1, Bridget McInnes, Terrence Adam, Ying Liu, Ted Pedersen, Genevieve B Melton.   

Abstract

Automated approaches to measuring semantic similarity and relatedness can provide necessary semantic context information for information retrieval applications and a number of fundamental natural language processing tasks including word sense disambiguation. Challenges for the development of these approaches include the limited availability of validated reference standards and the need for better understanding of the notions of semantic relatedness and similarity in medical vocabulary. We present results of a study in which eight medical residents were asked to judge 724 pairs of medical terms for semantic similarity and relatedness. The results of the study confirm the existence of a measurable mental representation of semantic relatedness between medical terms that is distinct from similarity and independent of the context in which the terms occur. This study produced a validated publicly available dataset for developing automated approaches to measuring semantic relatedness and similarity.

Mesh:

Year:  2010        PMID: 21347043      PMCID: PMC3041430     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  10 in total

1.  Using semantic distance for the efficient coding of medical concepts.

Authors:  C Bousquet; M C Jaulent; G Chatellier; P Degoulet
Journal:  Proc AMIA Symp       Date:  2000

2.  Towards the development of a conceptual distance metric for the UMLS.

Authors:  Jorge E Caviedes; James J Cimino
Journal:  J Biomed Inform       Date:  2004-04       Impact factor: 6.317

3.  Appraisal of the MedDRA conceptual structure for describing and grouping adverse drug reactions.

Authors:  Cédric Bousquet; Georges Lagier; Agnès Lillo-Le Louët; Christine Le Beller; Alain Venot; Marie-Christine Jaulent
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

4.  Measures of semantic similarity and relatedness in the biomedical domain.

Authors:  Ted Pedersen; Serguei V S Pakhomov; Siddharth Patwardhan; Christopher G Chute
Journal:  J Biomed Inform       Date:  2006-06-10       Impact factor: 6.317

5.  A cluster-based approach for semantic similarity in the biomedical domain.

Authors:  Hisham Al-Mubaid; Hoa A Nguyen
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

6.  Comparison of ontology-based semantic-similarity measures.

Authors:  Wei-Nchih Lee; Nigam Shah; Karanjot Sundlass; Mark Musen
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

Review 7.  Intraclass correlations: uses in assessing rater reliability.

Authors:  P E Shrout; J L Fleiss
Journal:  Psychol Bull       Date:  1979-03       Impact factor: 17.737

8.  Predicting human brain activity associated with the meanings of nouns.

Authors:  Tom M Mitchell; Svetlana V Shinkareva; Andrew Carlson; Kai-Min Chang; Vicente L Malave; Robert A Mason; Marcel Adam Just
Journal:  Science       Date:  2008-05-30       Impact factor: 47.728

9.  UMLS-Interface and UMLS-Similarity : open source software for measuring paths and semantic similarity.

Authors:  Bridget T McInnes; Ted Pedersen; Serguei V S Pakhomov
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

10.  Predicting judged similarity of natural categories from their neural representations.

Authors:  Matthew Weber; Sharon L Thompson-Schill; Daniel Osherson; James Haxby; Lawrence Parsons
Journal:  Neuropsychologia       Date:  2008-12-31       Impact factor: 3.139

  10 in total
  51 in total

1.  A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts.

Authors:  Rimma Pivovarov; Noémie Elhadad
Journal:  J Biomed Inform       Date:  2012-01-25       Impact factor: 6.317

2.  A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study.

Authors:  Serguei V S Pakhomov; Laura S Hemmy
Journal:  Cortex       Date:  2013-06-14       Impact factor: 4.027

3.  A comparison of word embeddings for the biomedical natural language processing.

Authors:  Yanshan Wang; Sijia Liu; Naveed Afzal; Majid Rastegar-Mojarad; Liwei Wang; Feichen Shen; Paul Kingsbury; Hongfang Liu
Journal:  J Biomed Inform       Date:  2018-09-12       Impact factor: 6.317

4.  Towards a framework for developing semantic relatedness reference standards.

Authors:  Serguei V S Pakhomov; Ted Pedersen; Bridget McInnes; Genevieve B Melton; Alexander Ruggieri; Christopher G Chute
Journal:  J Biomed Inform       Date:  2010-10-31       Impact factor: 6.317

5.  U-path: An undirected path-based measure of semantic similarity.

Authors:  Bridget T McInnes; Ted Pedersen; Ying Liu; Genevieve B Melton; Serguei V Pakhomov
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

6.  Enhancing clinical concept extraction with contextual embeddings.

Authors:  Yuqi Si; Jingqi Wang; Hua Xu; Kirk Roberts
Journal:  J Am Med Inform Assoc       Date:  2019-11-01       Impact factor: 4.497

7.  deepBioWSD: effective deep neural word sense disambiguation of biomedical text data.

Authors:  Ahmad Pesaranghader; Stan Matwin; Marina Sokolova; Ali Pesaranghader
Journal:  J Am Med Inform Assoc       Date:  2019-05-01       Impact factor: 4.497

8.  Feature extraction for phenotyping from semantic and knowledge resources.

Authors:  Wenxin Ning; Stephanie Chan; Andrew Beam; Ming Yu; Alon Geva; Katherine Liao; Mary Mullen; Kenneth D Mandl; Isaac Kohane; Tianxi Cai; Sheng Yu
Journal:  J Biomed Inform       Date:  2019-02-07       Impact factor: 6.317

9.  Embedding of semantic predications.

Authors:  Trevor Cohen; Dominic Widdows
Journal:  J Biomed Inform       Date:  2017-03-08       Impact factor: 6.317

10.  Using SemRep to label semantic relations extracted from clinical text.

Authors:  Ying Liu; Robert Bill; Marcelo Fiszman; Thomas Rindflesch; Ted Pedersen; Genevieve B Melton; Serguei V Pakhomov
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03
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