Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents

Ontologies and other schemes are useful for allowing semantic tagging of documents for many applications on the semantic web. Representing uncertainty on the semantic web is becoming increasingly common, using fuzzy ontologies and other techniques. Very large ontologies and vocabularies have been created, however users may find it difficult to select the correct concept or term when there are large numbers of items that on face value appear to represent the same idea. Creating subsets of ontologies is a popular approach to solving this problem but this may not fit well with the need to deal with complex domains. However crowdsourcing techniques, which harness the power of large groups, may be more effective than document analysis or expert opinion. In Crowdsourcing, large numbers of people collaborate by performing relatively simple tasks usually using applications distributed via the World Wide Web. This approach is being tested in the medical domain using a very large clinical vocabulary, SNOMED CT.

[1]  Elena Paslaru Bontas Simperl,et al.  Human Intelligence in the Process of Semantic Content Creation , 2010, World Wide Web.

[2]  Michael J. Muller,et al.  Getting our head in the clouds: toward evaluation studies of tagclouds , 2007, CHI.

[3]  B. Obama Modern health care for all Americans. , 2008, The New England journal of medicine.

[4]  Tim O'Reilly,et al.  What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software , 2007 .

[5]  James J. Cimino,et al.  Reliability of SNOMED-CT Coding by Three Physicians using Two Terminology Browsers , 2006, AMIA.

[6]  C Payne,et al.  Read Codes Version 3: A User Led Terminology , 1995, Methods of Information in Medicine.

[7]  Carole M Cotter Making the case for a clinical information system: the chief information officer view. , 2007, Journal of critical care.

[8]  Marco Loregian,et al.  Using Dynamic Fuzzy Ontologies to Understand Creative Environments , 2006, FQAS.

[9]  John Yen,et al.  Using fuzzy ontology for query refinement in a personalized abstract search engine , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[10]  Manuel Blum,et al.  reCAPTCHA: Human-Based Character Recognition via Web Security Measures , 2008, Science.

[11]  F B ROGERS,et al.  Medical Subject Headings , 1948, Nature.

[12]  P. V. Biron,et al.  The HL7 Clinical Document Architecture. , 2001, Journal of the American Medical Informatics Association : JAMIA.

[13]  Takahiro Yamanoi,et al.  D'Amico Fuzzy Ontologies for the Semantic Web , 2006, FQAS.

[14]  Michael Kohnen,et al.  Content-based image retrieval in medical applications for picture archiving and communication systems , 2003, SPIE Medical Imaging.

[15]  Wendy Hall,et al.  The Semantic Web Revisited , 2006, IEEE Intelligent Systems.

[16]  John E. Mattison,et al.  Review: The HL7 Clinical Document Architecture , 2001, J. Am. Medical Informatics Assoc..

[17]  H. Zimmermann,et al.  OSI Reference Model - The ISO Model of Architecture for Open Systems Interconnection , 1980, IEEE Transactions on Communications.

[18]  Alan L. Rector,et al.  Developing SNOMED CT Subsets from Clinical Notes for Intensive Care Service , 2008 .

[19]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic Web , 2006, IEEE Transactions on Knowledge and Data Engineering.

[20]  Bernardo A. Huberman Crowdsourcing and Attention , 2008, Computer.

[21]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[22]  Clement J. McDonald,et al.  Development of the Logical Observation Identifier Names and Codes (LOINC) vocabulary. , 1998, Journal of the American Medical Informatics Association : JAMIA.

[23]  Dipak Kalra,et al.  The openEHR Foundation. , 2005, Studies in health technology and informatics.

[24]  David Parry,et al.  Fuzzification of a standard ontology to encourage reuse , 2004, Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004..

[25]  Dennis Wollersheim,et al.  Methodology for creating a sample subset of dynamic taxonomy to use in navigating medical text databases , 2002, Proceedings International Database Engineering and Applications Symposium.

[26]  John F. Hurdle,et al.  Measuring diagnoses: ICD code accuracy. , 2005, Health services research.

[27]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[28]  Kent A. Spackman,et al.  Rates of Change in a Large Clinical Terminology: Three Years Experience with SNOMED Clinical Terms , 2005, AMIA.

[29]  Stefanos D. Kollias,et al.  Uncertainty and the Semantic Web , 2006, IEEE Intelligent Systems.

[30]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[31]  S Tu,et al.  Section 5: Decision Support, Knowledge Representation and Management: Decision Support, Knowledge Representation and Management in Medicine , 2006, Yearbook of Medical Informatics.

[32]  Athanasios V. Vasilakos,et al.  Interoperable and adaptive fuzzy services for ambient intelligence applications , 2010, TAAS.

[33]  Satoshi Nakamura,et al.  Can social bookmarking enhance search in the web? , 2007, JCDL '07.

[34]  Erik T. Mueller,et al.  Open Mind Common Sense: Knowledge Acquisition from the General Public , 2002, OTM.

[35]  Peter G. Goldschmidt,et al.  HIT and MIS , 2005, Commun. ACM.