Automatic identification of treatment relations for medical ontology learning : an exploratory study
This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) s...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2014
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/101249 http://hdl.handle.net/10220/20046 http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-9.php |
| _version_ | 1811691492967710720 |
|---|---|
| author | Khoo, Christopher S. G. Lee, Chew-Hung Na, Jin-Cheon |
| author2 | Wee Kim Wee School of Communication and Information |
| author_facet | Wee Kim Wee School of Communication and Information Khoo, Christopher S. G. Lee, Chew-Hung Na, Jin-Cheon |
| author_sort | Khoo, Christopher S. G. |
| collection | NTU |
| description | This study is part of a project to develop an automatic method to build ontologies,
especially in a medical domain, from a document collection. An earlier study had investigated an
approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical
Language System) semantic net. The study found that semantic relations between concepts could be
inferred 68% of the time, although the method often could not distinguish between a few possible
relation types. Our current research focuses on the use of natural language processing techniques to
improve the identification of semantic relations. In particular, we explore both a semi-automatic and
manual construction of linguistic patterns for identifying treatment relations in medical abstracts in the
domain of colon cancer treatment. Association rule mining was applied to sample sentences containing
both a disease concept and a reference to drug, to identify frequently occurring word patterns to see if
these patterns could be used to identify treatment relations in sentences. This did not yield many useful
patterns, suggesting that statistical association measures have to be complemented with syntactic and
semantic constraints to identify useful patterns. In the second part of the study, linguistic patterns were
manually constructed based on the same sentences. This yielded promising results. Work is ongoing
to improve the manually constructed patterns as well as to identify the syntactic and semantic
constraints that can be used to improve the automatic construction of linguistic patterns. |
| first_indexed | 2024-10-01T06:20:46Z |
| format | Conference Paper |
| id | ntu-10356/101249 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:20:46Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1012492019-12-06T20:35:38Z Automatic identification of treatment relations for medical ontology learning : an exploratory study Khoo, Christopher S. G. Lee, Chew-Hung Na, Jin-Cheon Wee Kim Wee School of Communication and Information International ISKO Conference (8th : 2004 : London) Communication and Information This study is part of a project to develop an automatic method to build ontologies, especially in a medical domain, from a document collection. An earlier study had investigated an approach to inferring semantic relations between medical concepts using the UMLS (Unified Medical Language System) semantic net. The study found that semantic relations between concepts could be inferred 68% of the time, although the method often could not distinguish between a few possible relation types. Our current research focuses on the use of natural language processing techniques to improve the identification of semantic relations. In particular, we explore both a semi-automatic and manual construction of linguistic patterns for identifying treatment relations in medical abstracts in the domain of colon cancer treatment. Association rule mining was applied to sample sentences containing both a disease concept and a reference to drug, to identify frequently occurring word patterns to see if these patterns could be used to identify treatment relations in sentences. This did not yield many useful patterns, suggesting that statistical association measures have to be complemented with syntactic and semantic constraints to identify useful patterns. In the second part of the study, linguistic patterns were manually constructed based on the same sentences. This yielded promising results. Work is ongoing to improve the manually constructed patterns as well as to identify the syntactic and semantic constraints that can be used to improve the automatic construction of linguistic patterns. Accepted version 2014-07-03T05:02:47Z 2019-12-06T20:35:38Z 2014-07-03T05:02:47Z 2019-12-06T20:35:38Z 2004 2004 Conference Paper Lee, C.-H., Khoo, C. S. G, & Na, J.-C. (2004). Automatic identification of treatment relations for medical ontology learning: an exploratory study. In I.C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pp. 245-250). Wurzburg, Germany: Ergon Verlag. https://hdl.handle.net/10356/101249 http://hdl.handle.net/10220/20046 http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-9.php en © 2004 International ISKO Conference. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of the Eighth International ISKO Conference, International ISKO Conference. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [URL: http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-9.php]. application/pdf |
| spellingShingle | Communication and Information Khoo, Christopher S. G. Lee, Chew-Hung Na, Jin-Cheon Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title | Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title_full | Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title_fullStr | Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title_full_unstemmed | Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title_short | Automatic identification of treatment relations for medical ontology learning : an exploratory study |
| title_sort | automatic identification of treatment relations for medical ontology learning an exploratory study |
| topic | Communication and Information |
| url | https://hdl.handle.net/10356/101249 http://hdl.handle.net/10220/20046 http://www.ergon-verlag.de/bibliotheks--informationswissenschaft/advances-in-knowledge-organization/band-9.php |
| work_keys_str_mv | AT khoochristophersg automaticidentificationoftreatmentrelationsformedicalontologylearninganexploratorystudy AT leechewhung automaticidentificationoftreatmentrelationsformedicalontologylearninganexploratorystudy AT najincheon automaticidentificationoftreatmentrelationsformedicalontologylearninganexploratorystudy |